lab9.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Lab 9\n",
    "\n",
    "## TODO:\n",
    "\n",
    "- [x] Linear classifier\n",
    "  - [x] Use training data, note MSE\n",
    "  - [x] Can we say the data is linearly separable?\n",
    "  - [x] Note MSE on test data\n",
    "  - [x] Does the model generalise?\n",
    "- [x] Logistic regression\n",
    "  - [x] Use training data, note MSE\n",
    "  - [x] Note MSE on test data\n",
    "  - [x] Record and explain findings\n",
    "- [x] (optional) Experiment with hyperparameters (maybe use random search)\n",
    "  - [x] learning rate\n",
    "  - [x] epochs\n",
    "  - [-] batch size\n",
    "- [x] (optional) Table or graph results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.linear_model import SGDClassifier, LogisticRegression\n",
    "from scipy.stats import randint\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>failures</th>\n",
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       "      <th>studytime</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
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       "      <th>reason_reputation</th>\n",
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       "      failures  higher  studytime  Medu  Fedu  Dalc  age  reason_reputation  \\\n",
       "0            0       1          2     4     4     1   18                  0   \n",
       "1            0       1          2     1     1     1   17                  0   \n",
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       "4            0       1          2     3     3     1   16                  0   \n",
       "...        ...     ...        ...   ...   ...   ...  ...                ...   \n",
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       "1042         0       1          1     3     1     3   17                  0   \n",
       "1043         0       1          1     3     2     3   18                  0   \n",
       "\n",
       "      school  address  internet  G3  \n",
       "0          0        0         0   6  \n",
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       "...      ...      ...       ...  ..  \n",
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       "1040       1        0         1  16  \n",
       "1041       1        0         0   9  \n",
       "1042       1        0         1  10  \n",
       "1043       1        1         1  11  \n",
       "\n",
       "[1044 rows x 12 columns]"
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     "metadata": {},
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   "source": [
    "np.random.seed(0)\n",
    "os.environ[\"OMP_NUM_THREADS\"] = \"5\"\n",
    "\n",
    "df3 = pd.read_csv(\"Data/data3.csv\")\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      failures  higher  studytime  Medu  Fedu  Dalc  age  reason_reputation  \\\n",
       "0            0       1          2     4     4     1   18                  0   \n",
       "1            0       1          2     1     1     1   17                  0   \n",
       "2            3       1          2     1     1     2   15                  0   \n",
       "3            0       1          3     4     2     1   15                  0   \n",
       "4            0       1          2     3     3     1   16                  0   \n",
       "...        ...     ...        ...   ...   ...   ...  ...                ...   \n",
       "1039         1       1          3     2     3     1   19                  0   \n",
       "1040         0       1          2     3     1     1   18                  0   \n",
       "1041         0       1          2     1     1     1   18                  0   \n",
       "1042         0       1          1     3     1     3   17                  0   \n",
       "1043         0       1          1     3     2     3   18                  0   \n",
       "\n",
       "      school  address  internet  \n",
       "0          0        0         0  \n",
       "1          0        0         1  \n",
       "2          0        0         1  \n",
       "3          0        0         1  \n",
       "4          0        0         0  \n",
       "...      ...      ...       ...  \n",
       "1039       1        1         1  \n",
       "1040       1        0         1  \n",
       "1041       1        0         0  \n",
       "1042       1        0         1  \n",
       "1043       1        1         1  \n",
       "\n",
       "[1044 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df3.copy()\n",
    "y = X.pop(\"G3\") # y becomes the G3 column and deletes it from df3_copy\n",
    "y_binary = [0 if y_i > np.mean(y) else 1 for y_i in y] #making the target class into binary (over/under average score)\n",
    "\n",
    "# display(X)\n",
    "# print(y_binary)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scale\n",
    "scaler = MinMaxScaler()\n",
    "X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(835, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y_binary, test_size=0.2, stratify=y_binary, random_state=42,)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=SGDClassifier(loss=&#x27;squared_error&#x27;),\n",
       "             param_grid={&#x27;alpha&#x27;: [1.0, 0.1, 0.01, 0.001],\n",
       "                         &#x27;max_iter&#x27;: [2, 5, 10, 100],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;none&#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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=SGDClassifier(loss=&#x27;squared_error&#x27;),\n",
       "             param_grid={&#x27;alpha&#x27;: [1.0, 0.1, 0.01, 0.001],\n",
       "                         &#x27;max_iter&#x27;: [2, 5, 10, 100],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;none&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(loss=&#x27;squared_error&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(loss=&#x27;squared_error&#x27;)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SGDClassifier(loss='squared_error'),\n",
       "             param_grid={'alpha': [1.0, 0.1, 0.01, 0.001],\n",
       "                         'max_iter': [2, 5, 10, 100],\n",
       "                         'penalty': ['l1', 'l2', 'none']})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#batch size is not supported in SGDClassifier, can be done using partial_fit\n",
    "parameters = {\n",
    "    \"alpha\": [1.0, 0.1, 0.01, 0.001],\n",
    "    \"max_iter\": [2, 5, 10, 100],\n",
    "    \"penalty\" : [\"l1\", \"l2\", \"none\"]\n",
    "\n",
    "}\n",
    "linear_model = SGDClassifier(loss=\"squared_error\")\n",
    "linear_cv = GridSearchCV(linear_model, parameters, cv=10)\n",
    "linear_cv.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.6646706586826348\n",
      "Test accuracy: 0.6746411483253588\n"
     ]
    }
   ],
   "source": [
    "print(f\"Train accuracy: {linear_cv.best_estimator_.score(X_train, y_train)}\")\n",
    "print(f\"Test accuracy: {linear_cv.best_estimator_.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>max_iter</th>\n",
       "      <th>penalty</th>\n",
       "      <th>classifier</th>\n",
       "      <th>test_method</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.000</td>\n",
       "      <td>100</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.649139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000</td>\n",
       "      <td>5</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.646730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000</td>\n",
       "      <td>10</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.639644</td>\n",
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       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.001</td>\n",
       "      <td>100</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.639501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.001</td>\n",
       "      <td>100</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.634653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000</td>\n",
       "      <td>2</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.633635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.000</td>\n",
       "      <td>5</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.628744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.623853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.001</td>\n",
       "      <td>100</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.616968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000</td>\n",
       "      <td>10</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.605967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.000</td>\n",
       "      <td>100</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.569220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.100</td>\n",
       "      <td>100</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.553170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.001</td>\n",
       "      <td>5</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.544851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.542513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.535198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.100</td>\n",
       "      <td>5</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.533090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.100</td>\n",
       "      <td>5</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.531655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.010</td>\n",
       "      <td>100</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.529403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.010</td>\n",
       "      <td>2</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.524469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.522404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.010</td>\n",
       "      <td>10</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.522117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.010</td>\n",
       "      <td>100</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.518646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.100</td>\n",
       "      <td>10</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.512665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.000</td>\n",
       "      <td>100</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.511374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000</td>\n",
       "      <td>2</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.511374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000</td>\n",
       "      <td>10</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.511374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000</td>\n",
       "      <td>5</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.511374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.001</td>\n",
       "      <td>2</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.510055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.001</td>\n",
       "      <td>2</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.506627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.010</td>\n",
       "      <td>100</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.506383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.001</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.505479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.100</td>\n",
       "      <td>100</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.504217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.100</td>\n",
       "      <td>2</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.501793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.010</td>\n",
       "      <td>10</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.499498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.100</td>\n",
       "      <td>100</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.499383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.010</td>\n",
       "      <td>2</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.498422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.100</td>\n",
       "      <td>10</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.496974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.100</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.494507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.010</td>\n",
       "      <td>5</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.493460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.100</td>\n",
       "      <td>2</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.489859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.001</td>\n",
       "      <td>5</td>\n",
       "      <td>l2</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.488712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.010</td>\n",
       "      <td>5</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.488339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.010</td>\n",
       "      <td>10</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.487478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.001</td>\n",
       "      <td>5</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.482702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.010</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.476649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.100</td>\n",
       "      <td>10</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.476649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.100</td>\n",
       "      <td>5</td>\n",
       "      <td>l1</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.475531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.010</td>\n",
       "      <td>5</td>\n",
       "      <td>none</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.473236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    alpha  max_iter penalty     classifier test_method  accuracy\n",
       "10  1.000       100      l2  SGDClassifier        10CV  0.649139\n",
       "4   1.000         5      l2  SGDClassifier        10CV  0.646730\n",
       "7   1.000        10      l2  SGDClassifier        10CV  0.639644\n",
       "46  0.001       100      l2  SGDClassifier        10CV  0.639501\n",
       "47  0.001       100    none  SGDClassifier        10CV  0.634653\n",
       "1   1.000         2      l2  SGDClassifier        10CV  0.633635\n",
       "5   1.000         5    none  SGDClassifier        10CV  0.628744\n",
       "2   1.000         2    none  SGDClassifier        10CV  0.623853\n",
       "45  0.001       100      l1  SGDClassifier        10CV  0.616968\n",
       "8   1.000        10    none  SGDClassifier        10CV  0.605967\n",
       "11  1.000       100    none  SGDClassifier        10CV  0.569220\n",
       "22  0.100       100      l2  SGDClassifier        10CV  0.553170\n",
       "39  0.001         5      l1  SGDClassifier        10CV  0.544851\n",
       "44  0.001        10    none  SGDClassifier        10CV  0.542513\n",
       "42  0.001        10      l1  SGDClassifier        10CV  0.535198\n",
       "17  0.100         5    none  SGDClassifier        10CV  0.533090\n",
       "16  0.100         5      l2  SGDClassifier        10CV  0.531655\n",
       "33  0.010       100      l1  SGDClassifier        10CV  0.529403\n",
       "24  0.010         2      l1  SGDClassifier        10CV  0.524469\n",
       "43  0.001        10      l2  SGDClassifier        10CV  0.522404\n",
       "31  0.010        10      l2  SGDClassifier        10CV  0.522117\n",
       "34  0.010       100      l2  SGDClassifier        10CV  0.518646\n",
       "20  0.100        10    none  SGDClassifier        10CV  0.512665\n",
       "9   1.000       100      l1  SGDClassifier        10CV  0.511374\n",
       "0   1.000         2      l1  SGDClassifier        10CV  0.511374\n",
       "6   1.000        10      l1  SGDClassifier        10CV  0.511374\n",
       "3   1.000         5      l1  SGDClassifier        10CV  0.511374\n",
       "36  0.001         2      l1  SGDClassifier        10CV  0.510055\n",
       "37  0.001         2      l2  SGDClassifier        10CV  0.506627\n",
       "35  0.010       100    none  SGDClassifier        10CV  0.506383\n",
       "38  0.001         2    none  SGDClassifier        10CV  0.505479\n",
       "23  0.100       100    none  SGDClassifier        10CV  0.504217\n",
       "12  0.100         2      l1  SGDClassifier        10CV  0.501793\n",
       "30  0.010        10      l1  SGDClassifier        10CV  0.499498\n",
       "21  0.100       100      l1  SGDClassifier        10CV  0.499383\n",
       "25  0.010         2      l2  SGDClassifier        10CV  0.498422\n",
       "19  0.100        10      l2  SGDClassifier        10CV  0.496974\n",
       "14  0.100         2    none  SGDClassifier        10CV  0.494507\n",
       "28  0.010         5      l2  SGDClassifier        10CV  0.493460\n",
       "13  0.100         2      l2  SGDClassifier        10CV  0.489859\n",
       "40  0.001         5      l2  SGDClassifier        10CV  0.488712\n",
       "27  0.010         5      l1  SGDClassifier        10CV  0.488339\n",
       "32  0.010        10    none  SGDClassifier        10CV  0.487478\n",
       "41  0.001         5    none  SGDClassifier        10CV  0.482702\n",
       "26  0.010         2    none  SGDClassifier        10CV  0.476649\n",
       "18  0.100        10      l1  SGDClassifier        10CV  0.476649\n",
       "15  0.100         5      l1  SGDClassifier        10CV  0.475531\n",
       "29  0.010         5    none  SGDClassifier        10CV  0.473236"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = pd.DataFrame(linear_cv.cv_results_[\"params\"])\n",
    "table[\"classifier\"] = \"SGDClassifier\"\n",
    "table[\"test_method\"] = \"10CV\"\n",
    "table[\"accuracy\"] = linear_cv.cv_results_[\"mean_test_score\"]\n",
    "table = table.sort_values(by=[\"accuracy\"], ascending=False)\n",
    "\n",
    "display(table)\n",
    "\n",
    "plt.ylim(0,1)\n",
    "plt.scatter(table[\"max_iter\"], table[\"accuracy\"])\n",
    "plt.show()\n",
    "plt.bar(table[\"penalty\"], table[\"accuracy\"])\n",
    "plt.show()\n",
    "plt.scatter(table[\"alpha\"], table[\"accuracy\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;max_iter&#x27;: [2, 5, 10, 100],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;none&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;sag&#x27;,\n",
       "                                    &#x27;saga&#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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;max_iter&#x27;: [2, 5, 10, 100],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;none&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;newton-cg&#x27;, &#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;sag&#x27;,\n",
       "                                    &#x27;saga&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={'max_iter': [2, 5, 10, 100],\n",
       "                         'penalty': ['l1', 'l2', 'none'],\n",
       "                         'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag',\n",
       "                                    'saga']})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#batch size is not supported in SGDClassifier, can be done using partial_fit\n",
    "parameters = {\n",
    "    \"max_iter\": [2, 5, 10, 100],\n",
    "    \"penalty\" : [\"l1\", \"l2\", \"none\"],\n",
    "    \"solver\": [\"newton-cg\", \"lbfgs\", \"liblinear\", \"sag\", \"saga\"]}\n",
    "\n",
    "log_regression = LogisticRegression()\n",
    "logistic_cv = GridSearchCV(log_regression, parameters, cv=10)\n",
    "logistic_cv.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.6874251497005988\n",
      "Test accuracy: 0.69377990430622\n"
     ]
    }
   ],
   "source": [
    "print(f\"Train accuracy: {logistic_cv.best_estimator_.score(X_train, y_train)}\")\n",
    "print(f\"Test accuracy: {logistic_cv.best_estimator_.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_iter</th>\n",
       "      <th>penalty</th>\n",
       "      <th>solver</th>\n",
       "      <th>classifier</th>\n",
       "      <th>test_method</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>10</td>\n",
       "      <td>l2</td>\n",
       "      <td>sag</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.677897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>10</td>\n",
       "      <td>l2</td>\n",
       "      <td>saga</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.677883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.675502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>100</td>\n",
       "      <td>l2</td>\n",
       "      <td>saga</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.674312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>10</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.674312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>100</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.674312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>100</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.674312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>100</td>\n",
       "      <td>l2</td>\n",
       "      <td>sag</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.674312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>10</td>\n",
       "      <td>none</td>\n",
       "      <td>saga</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.674283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>10</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.674254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>l2</td>\n",
       "      <td>saga</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.674211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5</td>\n",
       "      <td>none</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.671902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>l2</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.671873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>10</td>\n",
       "      <td>l2</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.670683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>100</td>\n",
       "      <td>l2</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.670683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5</td>\n",
       "      <td>l2</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.670683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5</td>\n",
       "      <td>l1</td>\n",
       "      <td>saga</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.670683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5</td>\n",
       "      <td>l2</td>\n",
       "      <td>saga</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.670683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>10</td>\n",
       "      <td>none</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.669521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>100</td>\n",
       "      <td>none</td>\n",
       "      <td>saga</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.669521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>100</td>\n",
       "      <td>none</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.669521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>100</td>\n",
       "      <td>none</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.669521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>100</td>\n",
       "      <td>none</td>\n",
       "      <td>sag</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.669521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>10</td>\n",
       "      <td>none</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.669435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>100</td>\n",
       "      <td>l1</td>\n",
       "      <td>saga</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.668302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>l2</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.668302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5</td>\n",
       "      <td>none</td>\n",
       "      <td>saga</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.668287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5</td>\n",
       "      <td>l1</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.667126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>10</td>\n",
       "      <td>none</td>\n",
       "      <td>sag</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.667083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.665892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>10</td>\n",
       "      <td>l1</td>\n",
       "      <td>saga</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.664716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>10</td>\n",
       "      <td>l1</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.663540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>100</td>\n",
       "      <td>l1</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.662349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>l1</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.662321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>saga</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.662321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>l1</td>\n",
       "      <td>saga</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.659868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5</td>\n",
       "      <td>none</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.657573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.657544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5</td>\n",
       "      <td>none</td>\n",
       "      <td>sag</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.649139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>l2</td>\n",
       "      <td>sag</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.647806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>l2</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.639659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.638468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5</td>\n",
       "      <td>l2</td>\n",
       "      <td>sag</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.633491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>sag</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>0.591738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>l1</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>l1</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>l1</td>\n",
       "      <td>sag</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5</td>\n",
       "      <td>l1</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5</td>\n",
       "      <td>l1</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>l1</td>\n",
       "      <td>sag</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5</td>\n",
       "      <td>none</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>10</td>\n",
       "      <td>l1</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>10</td>\n",
       "      <td>l1</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>10</td>\n",
       "      <td>l1</td>\n",
       "      <td>sag</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>10</td>\n",
       "      <td>none</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>100</td>\n",
       "      <td>l1</td>\n",
       "      <td>newton-cg</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>100</td>\n",
       "      <td>l1</td>\n",
       "      <td>lbfgs</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>100</td>\n",
       "      <td>l1</td>\n",
       "      <td>sag</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>100</td>\n",
       "      <td>none</td>\n",
       "      <td>liblinear</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>10CV</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    max_iter penalty     solver          classifier test_method  accuracy\n",
       "38        10      l2        sag  LogisticRegression        10CV  0.677897\n",
       "39        10      l2       saga  LogisticRegression        10CV  0.677883\n",
       "20         5      l2  newton-cg  LogisticRegression        10CV  0.675502\n",
       "54       100      l2       saga  LogisticRegression        10CV  0.674312\n",
       "35        10      l2  newton-cg  LogisticRegression        10CV  0.674312\n",
       "51       100      l2      lbfgs  LogisticRegression        10CV  0.674312\n",
       "50       100      l2  newton-cg  LogisticRegression        10CV  0.674312\n",
       "53       100      l2        sag  LogisticRegression        10CV  0.674312\n",
       "44        10    none       saga  LogisticRegression        10CV  0.674283\n",
       "36        10      l2      lbfgs  LogisticRegression        10CV  0.674254\n",
       "9          2      l2       saga  LogisticRegression        10CV  0.674211\n",
       "25         5    none  newton-cg  LogisticRegression        10CV  0.671902\n",
       "7          2      l2  liblinear  LogisticRegression        10CV  0.671873\n",
       "37        10      l2  liblinear  LogisticRegression        10CV  0.670683\n",
       "52       100      l2  liblinear  LogisticRegression        10CV  0.670683\n",
       "22         5      l2  liblinear  LogisticRegression        10CV  0.670683\n",
       "19         5      l1       saga  LogisticRegression        10CV  0.670683\n",
       "24         5      l2       saga  LogisticRegression        10CV  0.670683\n",
       "40        10    none  newton-cg  LogisticRegression        10CV  0.669521\n",
       "59       100    none       saga  LogisticRegression        10CV  0.669521\n",
       "55       100    none  newton-cg  LogisticRegression        10CV  0.669521\n",
       "56       100    none      lbfgs  LogisticRegression        10CV  0.669521\n",
       "58       100    none        sag  LogisticRegression        10CV  0.669521\n",
       "41        10    none      lbfgs  LogisticRegression        10CV  0.669435\n",
       "49       100      l1       saga  LogisticRegression        10CV  0.668302\n",
       "5          2      l2  newton-cg  LogisticRegression        10CV  0.668302\n",
       "29         5    none       saga  LogisticRegression        10CV  0.668287\n",
       "17         5      l1  liblinear  LogisticRegression        10CV  0.667126\n",
       "43        10    none        sag  LogisticRegression        10CV  0.667083\n",
       "10         2    none  newton-cg  LogisticRegression        10CV  0.665892\n",
       "34        10      l1       saga  LogisticRegression        10CV  0.664716\n",
       "32        10      l1  liblinear  LogisticRegression        10CV  0.663540\n",
       "47       100      l1  liblinear  LogisticRegression        10CV  0.662349\n",
       "2          2      l1  liblinear  LogisticRegression        10CV  0.662321\n",
       "14         2    none       saga  LogisticRegression        10CV  0.662321\n",
       "4          2      l1       saga  LogisticRegression        10CV  0.659868\n",
       "26         5    none      lbfgs  LogisticRegression        10CV  0.657573\n",
       "21         5      l2      lbfgs  LogisticRegression        10CV  0.657544\n",
       "28         5    none        sag  LogisticRegression        10CV  0.649139\n",
       "8          2      l2        sag  LogisticRegression        10CV  0.647806\n",
       "6          2      l2      lbfgs  LogisticRegression        10CV  0.639659\n",
       "11         2    none      lbfgs  LogisticRegression        10CV  0.638468\n",
       "23         5      l2        sag  LogisticRegression        10CV  0.633491\n",
       "13         2    none        sag  LogisticRegression        10CV  0.591738\n",
       "0          2      l1  newton-cg  LogisticRegression        10CV       NaN\n",
       "1          2      l1      lbfgs  LogisticRegression        10CV       NaN\n",
       "3          2      l1        sag  LogisticRegression        10CV       NaN\n",
       "12         2    none  liblinear  LogisticRegression        10CV       NaN\n",
       "15         5      l1  newton-cg  LogisticRegression        10CV       NaN\n",
       "16         5      l1      lbfgs  LogisticRegression        10CV       NaN\n",
       "18         5      l1        sag  LogisticRegression        10CV       NaN\n",
       "27         5    none  liblinear  LogisticRegression        10CV       NaN\n",
       "30        10      l1  newton-cg  LogisticRegression        10CV       NaN\n",
       "31        10      l1      lbfgs  LogisticRegression        10CV       NaN\n",
       "33        10      l1        sag  LogisticRegression        10CV       NaN\n",
       "42        10    none  liblinear  LogisticRegression        10CV       NaN\n",
       "45       100      l1  newton-cg  LogisticRegression        10CV       NaN\n",
       "46       100      l1      lbfgs  LogisticRegression        10CV       NaN\n",
       "48       100      l1        sag  LogisticRegression        10CV       NaN\n",
       "57       100    none  liblinear  LogisticRegression        10CV       NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = pd.DataFrame(logistic_cv.cv_results_[\"params\"])\n",
    "table[\"classifier\"] = \"LogisticRegression\"\n",
    "table[\"test_method\"] = \"10CV\"\n",
    "table[\"accuracy\"] = logistic_cv.cv_results_[\"mean_test_score\"]\n",
    "table = table.sort_values(by=[\"accuracy\"], ascending=False)\n",
    "\n",
    "display(table)\n",
    "\n",
    "plt.ylim(0,1)\n",
    "plt.scatter(table[\"max_iter\"], table[\"accuracy\"])\n",
    "plt.show()\n",
    "plt.bar(table[\"penalty\"], table[\"accuracy\"])\n",
    "plt.show()\n",
    "plt.bar(table[\"solver\"], table[\"accuracy\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_pen(table):\n",
    "    plt.figure(figsize=(16, 10))\n",
    "    for i, col in enumerate(['l1', 'l2', 'none'], 1):\n",
    "        plt.subplot(1, 3, i)\n",
    "        plt.plot(table[table['penalty'] == col]['max_iter'], table[table['penalty'] == col]['accuracy'])\n",
    "        plt.ylim(0, table['accuracy'].max()+0.25)\n",
    "        plt.title(col)\n",
    "        plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "newton_table = table[table['solver'] == 'newton-cg'].sort_values(by=['max_iter'])\n",
    "plot_pen(newton_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lbfgs_table = table[table['solver'] == 'lbfgs'].sort_values(by=['max_iter'])\n",
    "plot_pen(lbfgs_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "liblinear_table = table[table['solver'] == 'liblinear'].sort_values(by=['max_iter'])\n",
    "plot_pen(liblinear_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sag_table = table[table['solver'] == 'sag'].sort_values(by=['max_iter'])\n",
    "plot_pen(sag_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "saga_table = table[table['solver'] == 'saga'].sort_values(by=['max_iter'])\n",
    "plot_pen(saga_table)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "07b9bce58800e0c6bedceed095b1f0040fd5a37e90a2446371ba89fcb226324d"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}