From c3ae9112e1009df5caa7924e4bff1635fb56576e Mon Sep 17 00:00:00 2001
From: Cat Flynn <me@ktyl.dev>
Date: Sun, 2 Mar 2025 00:11:57 +0000
Subject: [PATCH] cw1 submission

---
 cw1/spce0038_coursework_sklearn_MCMQ7.ipynb | 1836 +++++++++++++++++--
 1 file changed, 1709 insertions(+), 127 deletions(-)

diff --git a/cw1/spce0038_coursework_sklearn_MCMQ7.ipynb b/cw1/spce0038_coursework_sklearn_MCMQ7.ipynb
index b7f009a..5afea5d 100644
--- a/cw1/spce0038_coursework_sklearn_MCMQ7.ipynb
+++ b/cw1/spce0038_coursework_sklearn_MCMQ7.ipynb
@@ -680,7 +680,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7b51f6ec0c10>"
+       "<matplotlib.legend.Legend at 0x783476165590>"
       ]
      },
      "execution_count": 11,
@@ -1795,7 +1795,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7b51f6f68c10>"
+       "<matplotlib.legend.Legend at 0x783475e94750>"
       ]
      },
      "execution_count": 17,
@@ -2018,7 +2018,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7b51f6ec0c50>"
+       "<matplotlib.legend.Legend at 0x783473d66710>"
       ]
      },
      "execution_count": 21,
@@ -2395,7 +2395,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7b51f6d28ad0>"
+       "<matplotlib.legend.Legend at 0x783473c56fd0>"
       ]
      },
      "execution_count": 30,
@@ -3465,7 +3465,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7b51eeb88f10>"
+       "<matplotlib.legend.Legend at 0x783473d50490>"
       ]
      },
      "execution_count": 54,
@@ -4352,7 +4352,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7b51eeb69990>"
+       "<matplotlib.legend.Legend at 0x783471bcec90>"
       ]
      },
      "execution_count": 64,
@@ -4761,8 +4761,10 @@
    "source": [
     "Simplify $\\log(\\hat{p})$:\n",
     "\n",
-    "$$\\log(\\hat{p}) = \\log\\left(\\frac{1}{1+e^{-\\theta^Tx}}\\right)$$\n",
-    "$$= -\\log\\left(1+e^{-\\theta^Tx}\\right)$$\n",
+    "$$\\begin{eqnarray}\n",
+    "\\log(\\hat{p}) &=& \\log\\left(\\frac{1}{1+e^{-\\theta^Tx}}\\right)\\\\\n",
+    "&=& -\\log\\left(1+e^{-\\theta^Tx}\\right)\n",
+    "\\end{eqnarray}$$\n",
     "\n",
     "Simplify $\\log(1-\\hat{p})$:\n",
     "\n",
@@ -4798,6 +4800,13 @@
     "*Next, substitute these terms into the cost function and simplify to also express the cost function in terms linear in $\\log\\left({1+{\\rm e}^{-\\theta^{\\rm T}x}}\\right)$.*"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## "
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -4818,34 +4827,39 @@
     "$$\n",
     "C(\\theta) = -\\frac{1}{m} \\sum_{i=1}^m \n",
     "\\left [ \n",
-    "-y^{(i)} \\log(1+e^{-\\theta^Tx^{(i)}})\n",
+    "y^{(i)} (-\\log(1+e^{-\\theta^Tx^{(i)}}))\n",
     "+\n",
     "(1 - y^{(i)}) (-\\theta^Tx^{(i)}-\\log{(1+e^{-\\theta^Tx^{(i)}})})\n",
     "\\right]\n",
     "$$\n",
     "\n",
-    "We will just consider the inner expression while simplifying for notational brevity:\n",
+    "We extract the variable $u$, consider only the inner expression and drop the $(i)$ notation for brevity:\n",
+    "\n",
+    "$$ u = \\theta^Tx^{(i)} $$\n",
     "\n",
     "$$\n",
-    "-y \\log(1+e^{-\\theta^Tx^{(i)}})\n",
+    "y (-\\log(1+e^{-u}))\n",
     "+\n",
-    "(1 - y) (-\\theta^Tx-\\log{(1+e^{-\\theta^Tx})})\n",
+    "(1 - y) (-u-\\log{(1+e^{-u})})\n",
     "$$\n",
     "\n",
-    "\n",
-    "**Expand the last term...*\n",
+    "*Expand the terms...*\n",
     "\n",
     "$$\n",
-    "-y \\log(1+e^{-\\theta^Tx})\n",
-    "-\\theta^Tx \n",
-    "-\\log{(1+e^{-\\theta^Tx})} + y\\theta^Tx + y\\log{(1+e^{\\theta^Tx})}\n",
+    "-y\\log(1+e^{-u})\n",
+    "-u -\\log(1+e^{-u}) + yu +y\\log(1+e^{-u})\n",
     "$$\n",
     "\n",
+    "*Delete cancelling terms...*\n",
+    "\n",
+    "$$\n",
+    "-u -\\log(1+e^{-u}) + yu\n",
+    "$$\n",
     "\n",
     "*Simplify...*\n",
     "\n",
     "$$\n",
-    "(y-1)\\theta^Tx - \\log{(1+e^{-\\theta^Tx})}\n",
+    "(y-1)u - \\log(1+e^{-u})\n",
     "$$\n",
     "\n",
     "Yielding a simplified cost function:\n",
@@ -4855,9 +4869,7 @@
     "\\left [ \n",
     "(y^{(i)}-1)\\theta^Tx^{(i)} - \\log{(1+e^{-\\theta^Tx^{(i)}})}\n",
     "\\right]\n",
-    "$$\n",
-    "\n",
-    "\n"
+    "$$\n"
    ]
   },
   {
@@ -4895,51 +4907,55 @@
     }
    },
    "source": [
-    "We will differentiate $C(\\theta)$ term-by-term, writing only the inner expression for notational brevity.\n",
+    "We will differentiate $C(\\theta)$ term-by-term, writing only the inner expression and dropping the $(i)$ notation for brevity.\n",
     "\n",
     "\n",
-    "First term: $ (y^{(i)} - 1) \\theta^Tx^{(i)} $\n",
+    "First term: $ (y - 1) \\theta^Tx $\n",
     "\n",
-    "$$ \\frac{\\partial}{\\partial\\theta_j} \\left[ (y^{(i)}-1) \\theta^Tx^{(i)} \\right] = (y^{(i)}-1) \\frac{\\partial}{\\partial\\theta_j} \\theta^Tx^{(i)} $$ \n",
+    "$$ \\frac{\\partial}{\\partial\\theta_j} \\left[ (y-1) \\theta^Tx \\right] = (y-1) \\frac{\\partial}{\\partial\\theta_j} \\theta^Tx $$ \n",
     "\n",
-    "$$ \\frac{\\partial}{\\partial\\theta_j} \\theta^Tx^{(i)} = x^{(i)} $$\n",
+    "$$ \\frac{\\partial}{\\partial\\theta_j} \\theta^Tx = x_j $$\n",
     "\n",
-    "$$ \\frac{\\partial}{\\partial\\theta_j} \\left[ (y^{(i)}-1) \\theta^Tx^{(i)} \\right] = (y^{(i)}-1) x^{(i)} $$ \n",
+    "$$ \\frac{\\partial}{\\partial\\theta_j} \\left[ (y-1) \\theta^Tx \\right] = (y-1) \\cdot x_j $$ \n",
     "\n",
     "\n",
     "Second term: $ -\\log{(1+e^{-\\theta^Tx^{(i)}})} $\n",
     "\n",
-    "We use the chain rule to differentiate the function $ f(\\theta_j) = -\\log{(1+e^{-\\theta^Tx^{(i)}})} $.\n",
+    "Extract the negation to simplify the differentiation:\n",
     "\n",
-    "First let $u = \\theta^Tx^{(i)} $:\n",
+    "$$ \\frac{\\partial}{\\partial\\theta_j} \\left[ -\\log(1+e^{-\\theta^Tx}) \\right] = -\\frac{\\partial}{\\partial\\theta_j} \\left[ \\log(1+e^{-\\theta^Tx}) \\right] $$\n",
     "\n",
-    "$$ f(\\theta_j) = -\\log{(1+e^{-u})} $$\n",
+    "Extract functions for the chain rule:\n",
     "\n",
-    "Differentiate $f(\\theta_j)$ with respect to $u$, finding the sigmoid function $\\sigma$:\n",
+    "$$ f(g) = \\log(g) $$\n",
+    "$$ g(u) = 1+e^{-u} $$\n",
+    "$$ u(\\theta_j) = \\theta^Tx $$\n",
     "\n",
-    "$$ \\frac{\\partial}{\\partial u} \\left[ -\\log{(1+e^{-u})} \\right] = \\frac{e^{-u}}{1+e^{-u}} = e^{-u} \\cdot \\sigma{(u)} $$\n",
+    "$$ -\\frac{\\partial}{\\partial\\theta_j} \\left[ \\log(1+e^{-\\theta^Tx}) \\right] = -\\frac{d}{d\\theta_j} f(g(u(\\theta_j))) $$\n",
     "\n",
-    "Differentiate $u$ with respect to $\\theta_j$:\n",
+    "Differentiate those functions:\n",
     "\n",
-    "$$ \\frac{\\partial u}{\\partial\\theta_j} = \\frac{\\partial}{\\partial\\theta_j} \\theta^Tx^{(i)} = x^{(i)}$$\n",
+    "$$ \\frac{df}{dg} = \\frac{1}{g} $$\n",
+    "$$ \\frac{dg}{du} = -e^{-u} $$\n",
+    "$$ \\frac{du}{d\\theta_j} = x $$\n",
     "\n",
-    "Combine the derivatives:\n",
+    "Combine the derivatives and return the negation:\n",
     "\n",
-    "$$ \\frac{\\partial}{\\partial\\theta_j} f(\\theta_j) = -x^{(i)}e^{-u} \\cdot \\sigma{(u)} $$\n",
+    "$$ \\frac{\\partial}{\\partial\\theta_j} \\left[ -\\log(1+e^{-\\theta^Tx}) \\right] = -\\frac{df}{dg} \\cdot \\frac{dg}{du} \\cdot \\frac{du}{d\\theta_j} = -\\frac{1}{1+e^{-\\theta^Tx}} \\cdot -e^{-\\theta^Tx} \\cdot x $$\n",
     "\n",
-    "Substitute back $ u = \\theta^Tx^{(i)} $:\n",
+    "$$ = \\frac{e^{-\\theta^Tx}}{1+e^{-\\theta^Tx}} \\cdot x $$\n",
     "\n",
-    "$$ \\frac{\\partial}{\\partial\\theta_j} f(\\theta) = -x^{(i)}e^{-\\theta^Tx^{(i)}} \\cdot \\sigma{(-\\theta^Tx^{(i)})} $$\n",
+    "At this point we recognise $ \\sigma(x) = \\frac{1}{1+e^{-x}} $ and show that $ \\frac{e^{-x}}{1+e^{-x}} = 1 -\\sigma(x) $:\n",
     "\n",
-    "Now combine the two terms for the full derivative with respect to $\\theta_j$\n",
+    "$$ 1 -\\sigma(x) = 1 -\\frac{1}{1+e^{-x}} = \\frac{1+e^{-x}}{1+e^{-x}} -\\frac{1}{1+e^{-x}} = \\frac{1+e^{-x}-1}{1+e^{-x}} = \\frac{e^{-x}}{1+e^{-x}} $$\n",
     "\n",
-    "$$ \\frac{\\partial C}{\\partial\\theta_j} = (y^{(i)}-1) x^{(i)} - x^{(i)}e^{-\\theta^Tx^{(i)}} \\cdot \\sigma{(-\\theta^Tx^{(i)})} $$\n",
+    "Thus:\n",
     "\n",
-    "Simplify...\n",
+    "$$ \\frac{\\partial}{\\partial\\theta_j} \\left[ -\\log(1+e^{-\\theta^Tx}) \\right] = (1 -\\sigma(\\theta^Tx)) \\cdot x $$\n",
     "\n",
-    "$$ \\frac{\\partial C}{\\partial\\theta_j} = x^{(i)} \\left[ y^{(i)} - 1 - e^{-\\theta^Tx^{(i)}} \\cdot \\sigma{(-\\theta^Tx^{(i)})} \\right] $$\n",
+    "We can now recombine the two terms:\n",
     "\n",
-    "---\n"
+    "$$ \\frac{\\partial C}{\\partial\\theta_j} = (y-1)x + (1-\\sigma\\left(\\theta^Tx\\right))x = \\left[ y -\\sigma(\\theta^Tx) \\right]x $$\n"
    ]
   },
   {
@@ -4977,7 +4993,17 @@
     }
    },
    "source": [
-    "YOUR ANSWER HERE (i give up)"
+    "We re-include the outer expression and $(i)$ notation:\n",
+    "\n",
+    "$$\n",
+    "\\frac{\\partial C}{\\partial \\theta} = \n",
+    "-\\frac{1}{m} \\sum_{i=1}^m \n",
+    "\\left[ \\sigma\\left(\\theta^{\\rm T} x^{(i)} \\right) - y^{(i)} \\right]\n",
+    "x^{(i)} =\n",
+    "\\frac{1}{m} \\sum_{i=1}^m \n",
+    "\\left[ y^{(i)} - \\sigma\\left(\\theta^{\\rm T} x^{(i)} \\right) \\right]\n",
+    "x^{(i)}\n",
+    "$$"
    ]
   },
   {
@@ -5170,41 +5196,24 @@
      "solution": true
     }
    },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'float' object cannot be interpreted as an integer",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[75], line 9\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# YOUR CODE HERE\u001b[39;00m\n\u001b[1;32m      7\u001b[0m m \u001b[38;5;241m=\u001b[39m X_train_1d_b\u001b[38;5;241m.\u001b[39mT\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m it \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mn_iterations\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m)\u001b[49m:\n\u001b[1;32m     10\u001b[0m     step \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39malpha \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39mm \u001b[38;5;241m*\u001b[39m sigmoid(theta\u001b[38;5;241m.\u001b[39mT \u001b[38;5;241m@\u001b[39m X_train_1d_b\u001b[38;5;241m.\u001b[39mT)\n\u001b[1;32m     11\u001b[0m     theta \u001b[38;5;241m=\u001b[39m theta \u001b[38;5;241m-\u001b[39m step\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_iterations = 4000\n",
     "alpha = 100.0\n",
     "theta = np.array([[1], [1]])\n",
     "\n",
     "# YOUR CODE HERE\n",
+    "m = len(X_train_1d_b)\n",
     "\n",
-    "m = X_train_1d_b.T.shape[1]\n",
-    "\n",
-    "for it in range(n_iterations/1000):\n",
-    "    step = -alpha * 1/m * sigmoid(theta.T @ X_train_1d_b.T)\n",
-    "    theta = theta - step\n",
-    "#while not np.isclose(gradient, 0) and iterations < 1000000:\n",
-    "#\n",
-    "#    theta = theta - \n",
-    "\n",
-    "#raise NotImplementedError()"
+    "for n in range(n_iterations):\n",
+    "    residuals = sigmoid(X_train_1d_b @ theta) - y_train.reshape(-1, 1)\n",
+    "    gradients = 1/m * X_train_1d_b.T @ residuals\n",
+    "    theta = theta - alpha * gradients\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -5219,7 +5228,17 @@
      "solution": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta defined.\n",
+      "theta[0] = -21.7012\n",
+      "theta[1] = 15.8177\n"
+     ]
+    }
+   ],
    "source": [
     "check_var_defined('theta')\n",
     "print(\"theta[0] = {0:.4f}\".format(theta[0][0]))\n",
@@ -5247,7 +5266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -5263,12 +5282,13 @@
    "outputs": [],
    "source": [
     "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "intercept_diff = (theta[0] - clf.coef_[0])[0]\n",
+    "#raise NotImplementedError()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 78,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -5283,7 +5303,16 @@
      "solution": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "intercept_diff defined.\n",
+      "intercept_diff = -3.4476E+01\n"
+     ]
+    }
+   ],
    "source": [
     "check_var_defined('intercept_diff')\n",
     "print(\"intercept_diff = {0:.4E}\".format(intercept_diff))"
@@ -5310,7 +5339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 79,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -5326,12 +5355,13 @@
    "outputs": [],
    "source": [
     "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "coeff_diff = (theta[1] - clf.coef_[0])[0]\n",
+    "######raise NotImplementedError()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -5346,7 +5376,16 @@
      "solution": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "coeff_diff defined.\n",
+      "coeff_diff = 3.0429E+00\n"
+     ]
+    }
+   ],
    "source": [
     "check_var_defined('coeff_diff')\n",
     "print(\"coeff_diff = {0:.4E}\".format(coeff_diff))"
@@ -5406,7 +5445,7 @@
     }
    },
    "source": [
-    "YOUR ANSWER HERE"
+    "There are far more elements in one class than the other which causes a bias in the model which makes it difficult to detect istances of the smaller class. In the classification problem at hand, we find that the decision boundary, given by $\\beta_0/\\beta_1$, is 1.4. All training data lies on one side of this boundary, and thus every prediction is a background star."
    ]
   },
   {
@@ -5434,7 +5473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 81,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -5447,16 +5486,447 @@
      "solution": true
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(class_weight=&#x27;balanced&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(class_weight=&#x27;balanced&#x27;)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression(class_weight='balanced')"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
     "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "clf_balanced = LogisticRegression(class_weight=\"balanced\")\n",
+    "clf_balanced.fit(X_train_1d, y_train)\n",
+    "#raise NotImplementedError()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 82,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -5471,7 +5941,15 @@
      "solution": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "clf_balanced defined.\n"
+     ]
+    }
+   ],
    "source": [
     "check_var_defined('clf_balanced')"
    ]
@@ -5499,7 +5977,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 83,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -5515,12 +5993,13 @@
    "outputs": [],
    "source": [
     "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "decision_boundary_sklearn_balanced = (clf_balanced.intercept_/clf_balanced.coef_)[0][0]\n",
+    "#raise NotImplementedError()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 84,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -5535,7 +6014,16 @@
      "solution": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "decision_boundary_sklearn_balanced defined.\n",
+      "decision_boundary_sklearn_balanced = -1.0591\n"
+     ]
+    }
+   ],
    "source": [
     "check_var_defined('decision_boundary_sklearn_balanced')\n",
     "assert not hasattr(decision_boundary_sklearn_balanced, \"__len__\")\n",
@@ -5563,7 +6051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 85,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -5579,12 +6067,13 @@
    "outputs": [],
    "source": [
     "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "y_1d_proba_balanced = clf_balanced.predict_proba(X_1d_new)\n",
+    "#raise NotImplementedError()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 86,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -5599,7 +6088,15 @@
      "solution": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "y_1d_proba_balanced defined.\n"
+     ]
+    }
+   ],
    "source": [
     "check_var_defined('y_1d_proba_balanced')"
    ]
@@ -5625,7 +6122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 87,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -5639,11 +6136,34 @@
      "solution": true
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x783471a78f10>"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plot_scatter()\n",
     "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "plt.plot(X_1d_new, y_1d_proba_balanced, label=[\"Background\", \"RR Lyrae\"])\n",
+    "plt.legend()\n",
+    "#raise NotImplementedError()"
    ]
   },
   {
@@ -5681,7 +6201,7 @@
     }
    },
    "source": [
-    "YOUR ANSWER HERE"
+    "Now half the data would be predicted as RR Lyrae, which is still incorrect as there are far more background stars. Predicting in 1D will not work as the RR Lyrae class is intermixed in this dimension with bacground stars."
    ]
   },
   {
@@ -5724,7 +6244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 88,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -5738,7 +6258,18 @@
      "solution": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['u-g', 'g-r']"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ind = 1\n",
     "cols[:ind+1]"
@@ -5765,7 +6296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 89,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -5808,7 +6339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 90,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -5821,15 +6352,446 @@
      "solution": true
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(class_weight=&#x27;balanced&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(class_weight=&#x27;balanced&#x27;)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression(class_weight='balanced')"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "clf_2d_logistic = LogisticRegression(class_weight=\"balanced\")\n",
+    "clf_2d_logistic.fit(X_train_2d, y_train)\n",
+    "#raise NotImplementedError()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 91,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -5844,7 +6806,15 @@
      "solution": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "clf_2d_logistic defined.\n"
+     ]
+    }
+   ],
    "source": [
     "check_var_defined('clf_2d_logistic')"
    ]
@@ -5870,7 +6840,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 97,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -5886,12 +6856,46 @@
    "outputs": [],
    "source": [
     "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "\n",
+    "# Make a Pandas dataframe of the test data and predictions\n",
+    "df = pd.DataFrame({\n",
+    "    \"u-g\": [x[0] for x in X_test_2d],\n",
+    "    \"g-r\": [x[1] for x in X_test_2d],\n",
+    "    \"target\": pd.Series(y_test)\n",
+    "})\n",
+    "\n",
+    "# Make a prediction from the test data\n",
+    "y_pred_2d = clf_2d_logistic.predict(X_test_2d)\n",
+    "\n",
+    "df[\"prediction\"] = y_pred_2d\n",
+    "#print(test_df)\n",
+    "\n",
+    "# Precision = Tp/(Tp+Fp)\n",
+    "# Count number of true positives\n",
+    "positives = df[df[\"prediction\"] == 1]\n",
+    "true_positives = positives[positives[\"target\"] == 1]\n",
+    "false_positives = positives[positives[\"target\"] == 0]\n",
+    "#print(positives, true_positives, false_positives)\n",
+    "\n",
+    "Tp = len(true_positives)\n",
+    "Fp = len(false_positives)\n",
+    "precision_logistic = Tp/(Tp+Fp)\n",
+    "#print(precision_logistic)\n",
+    "\n",
+    "# Recall = Tp/(Tp+Fn)\n",
+    "negatives = df[df[\"prediction\"] == 0]\n",
+    "false_negatives = df[df[\"target\"] == 1]\n",
+    "\n",
+    "Fn = len(false_negatives)\n",
+    "recall_logistic = Tp/(Tp+Fn)\n",
+    "#print(recall_logistic)\n",
+    "\n",
+    "#raise NotImplementedError()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 98,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -5906,7 +6910,16 @@
      "solution": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "precision_logistic defined.\n",
+      "precision_logistic = 0.136511\n"
+     ]
+    }
+   ],
    "source": [
     "check_var_defined('precision_logistic')\n",
     "print(\"precision_logistic = {0:.6f}\".format(precision_logistic))"
@@ -5914,7 +6927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 99,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -5929,7 +6942,16 @@
      "solution": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "recall_logistic defined.\n",
+      "recall_logistic = 0.498024\n"
+     ]
+    }
+   ],
    "source": [
     "check_var_defined('recall_logistic')\n",
     "print(\"recall_logistic = {0:.6f}\".format(recall_logistic))"
@@ -5956,7 +6978,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 100,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -5999,7 +7021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 148,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -6013,10 +7035,56 @@
      "solution": true
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "from matplotlib import cm\n",
+    "\n",
+    "x_in = np.c_[xx.ravel(), yy.ravel()]\n",
+    "\n",
+    "def plot_classification_probability(probas):\n",
+    "\n",
+    "    contour = plt.contourf(xx, yy, np.array([p[1] for p in probas]).reshape(xx.shape), levels=100, cmap=\"coolwarm\")\n",
+    "    plt.xlabel(\"u-g\")\n",
+    "    plt.ylabel(\"g-r\")\n",
+    "    \n",
+    "    background = df[df[\"target\"] == 0]\n",
+    "    plt.scatter(background[\"u-g\"], background[\"g-r\"], label=\"Background\")\n",
+    "    \n",
+    "    rrlyrae = df[df[\"target\"] == 1]\n",
+    "    plt.scatter(rrlyrae[\"u-g\"], rrlyrae[\"g-r\"], label=\"RR Lyrae\")\n",
+    "    \n",
+    "    plt.colorbar(contour)\n",
+    "    \n",
+    "    plt.ylim(ylim)\n",
+    "    plt.title(\"Probability of RR Lyrae prediction\")\n",
+    "    plt.legend()\n",
+    "    \n",
+    "B0 = clf_2d_logistic.intercept_\n",
+    "B1 = clf_2d_logistic.coef_[0][0]\n",
+    "B2 = clf_2d_logistic.coef_[0][1]\n",
+    "\n",
+    "# Plot decision boundary\n",
+    "c = -B0/B2\n",
+    "m = -B1/B2\n",
+    "y0=m*xlim[0]+c\n",
+    "y1=m*xlim[1]+c\n",
+    "plt.plot(list(xlim), [y0, y1], color=\"yellow\", label=\"Decision boundary\")\n",
+    "\n",
+    "plot_classification_probability(clf_2d_logistic.predict_proba(x_in))\n",
+    "plt.show()\n",
+    "#raise NotImplementedError()"
    ]
   },
   {
@@ -6040,7 +7108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 147,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -6054,17 +7122,457 @@
      "solution": true
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-10 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-10 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-10 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-10 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-10 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-10 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-10 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(class_weight=&#x27;balanced&#x27;, probability=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(class_weight=&#x27;balanced&#x27;, probability=True)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "SVC(class_weight='balanced', probability=True)"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "clf_2d_svm = SVC(probability=True, class_weight=\"balanced\")\n",
+    "clf_2d_svm.fit(X_train_2d, y_train)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 149,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "clf_2d_svm defined.\n"
+     ]
+    }
+   ],
    "source": [
     "check_var_defined('clf_2d_svm')"
    ]
@@ -6090,7 +7598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 150,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -6104,10 +7612,26 @@
      "solution": true
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "\n",
+    "decision = clf_2d_svm.decision_function(x_in).reshape(xx.shape)\n",
+    "plt.contour(xx, yy, decision)\n",
+    "\n",
+    "plot_classification_probability(clf_2d_svm.predict_proba(x_in))\n",
+    "plt.show()"
    ]
   },
   {
@@ -6131,7 +7655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 161,
    "metadata": {
     "deletable": false,
     "nbgrader": {
@@ -6147,12 +7671,45 @@
    "outputs": [],
    "source": [
     "# YOUR CODE HERE\n",
-    "raise NotImplementedError()"
+    "# Make a Pandas dataframe of the test data and predictions\n",
+    "df_svm = pd.DataFrame({\n",
+    "    \"u-g\": [x[0] for x in X_test_2d],\n",
+    "    \"g-r\": [x[1] for x in X_test_2d],\n",
+    "    \"target\": pd.Series(y_test)\n",
+    "})\n",
+    "\n",
+    "# Make a prediction from the test data\n",
+    "y_pred_svm = clf_2d_svm.predict(X_test_2d)\n",
+    "\n",
+    "df_svm[\"prediction\"] = y_pred_svm\n",
+    "#print(test_df)\n",
+    "\n",
+    "# Precision = Tp/(Tp+Fp)\n",
+    "# Count number of true positives\n",
+    "positives = df_svm[df_svm[\"prediction\"] == 1]\n",
+    "true_positives = positives[positives[\"target\"] == 1]\n",
+    "false_positives = positives[positives[\"target\"] == 0]\n",
+    "#print(positives, true_positives, false_positives)\n",
+    "\n",
+    "Tp = len(true_positives)\n",
+    "Fp = len(false_positives)\n",
+    "precision_svm = Tp/(Tp+Fp)\n",
+    "#print(precision_logistic)\n",
+    "\n",
+    "# Recall = Tp/(Tp+Fn)\n",
+    "negatives = df_svm[df_svm[\"prediction\"] == 0]\n",
+    "false_negatives = df_svm[df_svm[\"target\"] == 1]\n",
+    "\n",
+    "Fn = len(false_negatives)\n",
+    "recall_svm = Tp/(Tp+Fn)\n",
+    "#print(recall_logistic)\n",
+    "\n",
+    "#raise NotImplementedError()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 158,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -6167,7 +7724,16 @@
      "solution": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "precision_svm defined.\n",
+      "precision_svm = 0.132154\n"
+     ]
+    }
+   ],
    "source": [
     "check_var_defined('precision_svm')\n",
     "print(\"precision_svm = {0:.6f}\".format(precision_svm))"
@@ -6175,7 +7741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 159,
    "metadata": {
     "deletable": false,
     "editable": false,
@@ -6190,7 +7756,16 @@
      "solution": false
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "recall_svm defined.\n",
+      "recall_svm = 0.500000\n"
+     ]
+    }
+   ],
    "source": [
     "check_var_defined('recall_svm')\n",
     "print(\"recall_svm = {0:.6f}\".format(recall_svm))"
@@ -6231,8 +7806,15 @@
     }
    },
    "source": [
-    "YOUR ANSWER HERE"
+    "The SVM model has a non-linear boundary which should be a better fit because the data is easier to separate in two dimensions. However judging by the precision and recall metrics it has not actually improved that much..."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {