2,2/Любаня homework_2_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задание\n",
    "\n",
    "Построить модель машинного обучения, позволяющую предсказать, является ли цветок цветком класса Ирис-Вирджиния или нет.\n",
    "\n",
    "### План решения:\n",
    "\n",
    "1. Из библиотеки scikit-learn подгрузите данные. Мы уже работали с ними в рамках Темы 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = load_iris(as_frame=True).frame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Сформируйте новое поле target_virginica, которое будет равно из 0, если цветок не является классом 2, иначе  — 1. Убедитесь, что разметка выполнена корректно, рассчитав количество объектов каждого класса по новой целевой переменной target_virginica."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target_virginica'] = df['target'].apply(lambda x: 1 if x == 2 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target_virginica  target\n",
       "0                 0         50\n",
       "                  1         50\n",
       "1                 2         50\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df.groupby('target_virginica')['target'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Обучите библиотечную модель логистической регрессии на обучающей части данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = df.drop(['target', 'target_virginica'], axis=1)\n",
    "target = df['target_virginica']\n",
    "\n",
    "features_train, features_val, target_train, target_val = train_test_split(features, target, test_size=0.2, random_state=42)\n",
    "\n",
    "log_reg_model = LogisticRegression()\n",
    "log_reg_model.fit(features_train, target_train)\n",
    "predictions_log_reg = log_reg_model.predict(features_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Оцените качество обученной на предыдущем шаге модели, выведя матрицу ошибок для прогнозов обученной моделью на тестовой части данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ошибок: 0\n",
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "def count_errors(true_answers, pred_answers):\n",
    "    all_answers_together = zip(true_answers, pred_answers)\n",
    "    errors_list = [1 if v[0] != v[1] else 0 for v in all_answers_together]\n",
    "    return sum(errors_list)\n",
    "\n",
    "print(\"Ошибок:\", count_errors(target_val, predictions_log_reg))\n",
    "\n",
    "print(\"Accuracy:\", accuracy_score(target_val, predictions_log_reg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x251c2725ae0>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay(confusion_matrix(target_val, predictions_log_reg)).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Вывод:\n",
    "\n",
    "Обученная модель прошла оценку по матрице ошибок со следующими показателями качества:\n",
    "- полнота = 1\n",
    "- точность = 1\n",
    "- F1-мера = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Сконструируйте свой класс MyLogisticRegression, решающий задачу логистической регрессии методом градиентного спуска. \n",
    "\n",
    "Он должен:\n",
    "- инициализироваться величиной шага градиентного спуска и количеством итераций градиентного спуска\n",
    "- содержать метод fit для обучения модели, на вход которой будут подаваться обучающие данные\n",
    "- чтобы реализовать функцию sigmoid, можно воспользоваться библиотечной функцией np.exp\n",
    "- для расчета градиента необходимо использовать формулу, приведенную в курсе в блоке «Логистическая регрессия для решения задачи бинарной классификации».\n",
    "- должен содержаться метод predict, который будет возвращать лейбл 0/1.\n",
    "- лейбл должен рассчитываться из вероятности принадлежности классу 1.\n",
    "- вероятность должна рассчитываться как сигмоида от результата произведения матрицы фичей X и вектора весов модели w."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyLogisticRegression:\n",
    "    def __init__(self, learning_rate=0.01, n_iterations=1000):\n",
    "        self.learning_rate = learning_rate\n",
    "        self.n_iterations = n_iterations\n",
    "        self.weights = None\n",
    "\n",
    "    def sigmoid(self, x):\n",
    "        return 1 / (1 + np.exp(-x))\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        n_samples, n_features = X.shape\n",
    "        self.weights = np.zeros(n_features)\n",
    "\n",
    "        for _ in range(self.n_iterations):\n",
    "            linear_model = np.dot(X, self.weights)\n",
    "            predictions = self.sigmoid(linear_model)\n",
    "\n",
    "            gradient = np.dot(X.T, (predictions - y)) / n_samples\n",
    "            self.weights -= self.learning_rate * gradient\n",
    "\n",
    "    def predict(self, X):\n",
    "        linear_model = np.dot(X, self.weights)\n",
    "        predictions = self.sigmoid(linear_model)\n",
    "        return np.where(predictions >= 0.5, 1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Обучите модель с помощью реализованного класса с произвольными параметрами градиентного спуска."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model = MyLogisticRegression(learning_rate=0.1, n_iterations=1000)\n",
    "my_model.fit(features_train, target_train)\n",
    "\n",
    "my_predictions = my_model.predict(features_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Оцените качество модели, обученной с помощью класса MyLogisticRegression, выведя матрицу ошибок для прогнозов обученной моделью на тестовой части данных. Подберите такие параметры градиентного спуска, чтобы разделение получилось не хуже, чем у библиотечной модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ошибок: 0\n",
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Ошибок:\", count_errors(target_val, my_predictions))\n",
    "\n",
    "print(\"Accuracy:\", accuracy_score(target_val, my_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x251c43a3cd0>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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33lJOTo5uu+22y173mmuuUVFRkdW2bt3qZWTM+QMA4BepqalKTU2t8Vh0dLTWr1/vsW/hwoX64Q9/qIKCAl1xxRW1XrdZs2aKi4uzFRsjfwCAI1x8w5+dJkklJSUerby83CfxnT59WoZhqE2bNpc8b//+/YqPj1f37t01fvx4FRQUeH0vkj8AwBEurva30yQpISFB0dHRVsvIyLAd27lz5zRjxgyNGzdOUVFRtZ6XlJSkzMxMrVu3TosXL1Z+fr4GDx6sM2fOeHU/yv4AAHihsLDQI0GHh4fbul5lZaXuvPNOmaapxYsXX/Lc704j9O/fX0lJSeratatWr16tyZMn1/meJH8AgCO4TUOGD17yExUVdcnRuTcuJv5Dhw7pww8/9Pq6bdq0Uc+ePZWbm+tVP8r+AABHsLXS/9vmSxcT//79+/XBBx+oXbt2Xl+jtLRUeXl56tSpk1f9SP4AAPhBaWmpsrOzlZ2dLUnKz89Xdna2CgoKVFlZqdtvv107duzQG2+8IZfLpeLiYhUXF6uiosK6xs0336yFCxda29OnT9emTZt08OBBbdu2TWPGjFFoaKjGjRvnVWyU/QEAjtDQr/fdsWOHhg0bZm2np6dLktLS0jRnzhytWbNGkjRgwACPfh999JGGDh0qScrLy9Px48etY4cPH9a4ceP09ddfq0OHDrrxxhv18ccfq0OHDl7FRvIHADhCQyf/oUOHyrzEXMGljl108OBBj+2VK1d6FUNtSP4AAEfw1YK/YMCcPwAADsPIHwDgCHZX7Pt6tX8gkfwBAI5wIfnbmfP3YTABRtkfAACHYeQPAHCEhl7t35iR/AEAjmB+2+z0DxaU/QEAcBhG/gAAR6DsX4XkDwBwBur+FpI/AMAZbI78FUQjf+b8AQBwGEb+AABH4A1/VUj+AABHYMFfFcr+AAA4DCN/AIAzmIa9RXtBNPIn+QMAHIE5/yqU/QEAcBhG/gAAZ+AlPxaSPwDAEVjtX6VOyX/NmjV1vuBtt91W72AAAID/1Sn5jx49uk4XMwxDLpfLTjwAAPhPEJXu7ahT8ne73f6OAwAAv6LsX8XWav9z5875Kg4AAPzL9EELEl4nf5fLpXnz5qlz585q3bq1Dhw4IEmaNWuWXnvtNZ8HCAAAfMvr5P/UU08pMzNTzzzzjMLCwqz91157rV599VWfBgcAgO8YPmjBwevk//rrr+vll1/W+PHjFRoaau1PTEzUvn37fBocAAA+Q9nf4nXy//LLL9WjR49q+91utyorK30SFAAA8B+vk3/fvn21ZcuWavv/8pe/6LrrrvNJUAAA+Bwjf4vXb/ibPXu20tLS9OWXX8rtduutt95STk6OXn/9da1du9YfMQIAYB9f9bN4PfIfNWqU3n33XX3wwQdq1aqVZs+erb179+rdd9/VLbfc4o8YAQCAD9Xr3f6DBw/W+vXrfR0LAAB+wyd9q9T7wz47duzQ3r17JV1YBzBw4ECfBQUAgM/xVT+L18n/8OHDGjdunP7xj3+oTZs2kqRTp05p0KBBWrlypbp06eLrGAEAgA95Pec/ZcoUVVZWau/evTpx4oROnDihvXv3yu12a8qUKf6IEQAA+y4u+LPTgoTXI/9NmzZp27Zt6tWrl7WvV69e+v3vf6/Bgwf7NDgAAHzFMC80O/2DhdfJPyEhocaX+bhcLsXHx/skKAAAfI45f4vXZf9nn31W999/v3bs2GHt27Fjhx588EH9z//8j0+DAwAAvlen5N+2bVvFxMQoJiZGkyZNUnZ2tpKSkhQeHq7w8HAlJSVp165duvvuu/0dLwAA9dPAc/6bN2/WyJEjFR8fL8MwlJWV5RmOaWr27Nnq1KmTWrRooeHDh2v//v2Xve6iRYt05ZVXKiIiQklJSfrnP//pVVxSHcv+L7zwgtcXBgCgUWngsn9ZWZkSExN19913a+zYsdWOP/PMM1qwYIGWL1+ubt26adasWUpJSdEXX3yhiIiIGq+5atUqpaena8mSJUpKStILL7yglJQU5eTkqGPHjnWOrU7JPy0trc4XBAAgmJWUlHhsX6yCf19qaqpSU1NrvIZpmnrhhRf02GOPadSoUZIufDU3NjZWWVlZ+vnPf15jv+eff15Tp07VpEmTJElLlizRe++9p6VLl2rmzJl1/g1ez/l/17lz51RSUuLRAABolHz0YZ+EhARFR0dbLSMjw+tQ8vPzVVxcrOHDh1v7oqOjlZSUpO3bt9fYp6KiQjt37vToExISouHDh9fapzZer/YvKyvTjBkztHr1an399dfVjrtcLm8vCQCA//mo7F9YWKioqChrd02j/sspLi6WJMXGxnrsj42NtY593/Hjx+VyuWrss2/fPq/u7/XI/5FHHtGHH36oxYsXKzw8XK+++qrmzp2r+Ph4vf76695eDgCAJiUqKsqj1Sf5B5rXyf/dd9/VH/7wB/3sZz9Ts2bNNHjwYD322GN6+umn9cYbb/gjRgAA7GtEb/iLi4uTJB09etRj/9GjR61j39e+fXuFhoZ61ac2Xif/EydOqHv37pIu/OvnxIkTkqQbb7xRmzdv9vZyAAA0iItv+LPTfKVbt26Ki4vThg0brH0lJSX65JNPlJycXGOfsLAwDRw40KOP2+3Whg0bau1TG6+Tf/fu3ZWfny9J6t27t1avXi3pQkXg4od+AABwutLSUmVnZys7O1vShUV+2dnZKigokGEYeuihh/Tkk09qzZo1+uyzzzRhwgTFx8dr9OjR1jVuvvlmLVy40NpOT0/XK6+8ouXLl2vv3r269957VVZWZq3+ryuvF/xNmjRJn376qYYMGaKZM2dq5MiRWrhwoSorK/X88897ezkAABpGAz/nv2PHDg0bNszaTk9Pl3Th8fnMzEw98sgjKisr0z333KNTp07pxhtv1Lp16zye8c/Ly9Px48et7bvuukvHjh3T7NmzVVxcrAEDBmjdunXVFgFejmGapq1CxqFDh7Rz50716NFD/fv3t3Mpr5WUlCg6Olon/91dUZG2nloEGq2U+AGBDgHwm/NmpTbqHZ0+fdpjBb0vXcwVV/z3kwppUfPLc+rC/c05Fcx4zK+xNhSvR/7f17VrV3Xt2tUXsQAA4DeGbH7Vz2eRBF6dkv+CBQvqfMEHHnig3sEAAAD/q1Pynz9/fp0uZhhGQJL/mJ791Mxo3uD3BRrC/tevD3QIgN+4vzkn3fNOw9zM7uN6PnzUL9DqlPwvru4HAKDJauAFf40Zq+QAAHAY2wv+AABoEhj5W0j+AABHsPuWPl++4S/QKPsDAOAwjPwBAM5A2d9Sr5H/li1b9Itf/ELJycn68ssvJUl//OMftXXrVp8GBwCAz5g+aEHC6+T/17/+VSkpKWrRooV2796t8vJySdLp06f19NNP+zxAAADgW14n/yeffFJLlizRK6+8oubNq16s8+Mf/1i7du3yaXAAAPhKY/qkb6B5Peefk5Ojm266qdr+6OhonTp1yhcxAQDge7zhz+L1yD8uLk65ubnV9m/dulXdu3f3SVAAAPgcc/4Wr5P/1KlT9eCDD+qTTz6RYRg6cuSI3njjDU2fPl333nuvP2IEAAA+5HXZf+bMmXK73br55pt19uxZ3XTTTQoPD9f06dN1//33+yNGAABs4yU/VbxO/oZh6Le//a0efvhh5ebmqrS0VH379lXr1q39ER8AAL7Bc/6Wer/kJywsTH379vVlLAAAoAF4nfyHDRsmw6h9xeOHH35oKyAAAPzC7uN6Th75DxgwwGO7srJS2dnZ2rNnj9LS0nwVFwAAvkXZ3+J18p8/f36N++fMmaPS0lLbAQEAAP/y2Vf9fvGLX2jp0qW+uhwAAL7Fc/4Wn33Vb/v27YqIiPDV5QAA8Cke9avidfIfO3asx7ZpmioqKtKOHTs0a9YsnwUGAAD8w+vkHx0d7bEdEhKiXr166YknntCtt97qs8AAAIB/eJX8XS6XJk2apH79+qlt27b+igkAAN9jtb/FqwV/oaGhuvXWW/l6HwCgyeGTvlW8Xu1/7bXX6sCBA/6IBQAANACvk/+TTz6p6dOna+3atSoqKlJJSYlHAwCg0eIxP0lezPk/8cQT+s1vfqOf/OQnkqTbbrvN4zW/pmnKMAy5XC7fRwkAgF3M+VvqnPznzp2rX/7yl/roo4/8GQ8AAPCzOid/07zwT54hQ4b4LRgAAPyFl/xU8epRv0t9zQ8AgEaNsr/Fq+Tfs2fPy/4D4MSJE7YCAgAA/uVV8p87d261N/wBANAUUPav4lXy//nPf66OHTv6KxYAAPyHsr+lzs/5M98PAEBwqHPyv7jaHwCAJsnOC37qUTW48sorZRhGtXbffffVeH5mZma1cyMiIurxQy+vzmV/t9vtlwAAAGgIDT3n/3//938eL77bs2ePbrnlFt1xxx219omKilJOTk7VPf1Udff6k74AADRJDTzn36FDB4/t3/3ud7rqqqsu+b4cwzAUFxdXn+i84vW7/QEAcLLvf9OmvLz8sn0qKir0pz/9SXffffclR/OlpaXq2rWrEhISNGrUKH3++ee+DN1C8gcAOIOP5vwTEhIUHR1ttYyMjMveOisrS6dOndLEiRNrPadXr15aunSp3nnnHf3pT3+S2+3WoEGDdPjw4Xr+4NpR9gcAOIKv5vwLCwsVFRVl7Q8PD79s39dee02pqamKj4+v9Zzk5GQlJydb24MGDVKfPn300ksvad68efUPvAYkfwAAvBAVFeWR/C/n0KFD+uCDD/TWW295dZ/mzZvruuuuU25urrchXhZlfwCAMzTwo34XLVu2TB07dtRPf/pTr/q5XC599tln6tSpU/1ufAmM/AEAjhCI1/u63W4tW7ZMaWlpatbMM+VOmDBBnTt3ttYMPPHEE/rRj36kHj166NSpU3r22Wd16NAhTZkypf5B14LkDwCAn3zwwQcqKCjQ3XffXe1YQUGBQkKqCvAnT57U1KlTVVxcrLZt22rgwIHatm2b+vbt6/O4SP4AAGcIwLv9b7311lrfkLtx40aP7fnz52v+/Pn1CMx7JH8AgDPwYR8LC/4AAHAYRv4AAEcwvm12+gcLkj8AwBko+1tI/gAARwjEo36NFXP+AAA4DCN/AIAzUPa3kPwBAM4RRAncDsr+AAA4DCN/AIAjsOCvCskfAOAMzPlbKPsDAOAwjPwBAI5A2b8KyR8A4AyU/S2U/QEAcBhG/gAAR6DsX4XkDwBwBsr+FpI/AMAZSP4W5vwBAHAYRv4AAEdgzr8KyR8A4AyU/S2U/QEAcBhG/gAARzBMU4ZZ/+G7nb6NDckfAOAMlP0tlP0BAHAYRv4AAEdgtX8Vkj8AwBko+1so+wMA4DCM/AEAjkDZvwrJHwDgDJT9LSR/AIAjMPKvwpw/AAAOw8gfAOAMlP0tJH8AgGMEU+neDsr+AAA4DCN/AIAzmOaFZqd/kCD5AwAcgdX+VSj7AwDgMCR/AIAzmD5oXpgzZ44Mw/BovXv3vmSfN998U71791ZERIT69eun999/37ub1hHJHwDgCIbbfvPWNddco6KiIqtt3bq11nO3bdumcePGafLkydq9e7dGjx6t0aNHa8+ePTZ+dc2Y8wcAwAslJSUe2+Hh4QoPD6/x3GbNmikuLq5O133xxRc1YsQIPfzww5KkefPmaf369Vq4cKGWLFliL+jvYeSPOhs58biWf/KF3j3wL724dr96DTgb6JCAeonYd0adns9Vtwc+09UTdqnVzlMex1v930nFP7Nf3e/9VFdP2KWwQ/xZDwo+KvsnJCQoOjraahkZGbXecv/+/YqPj1f37t01fvx4FRQU1Hru9u3bNXz4cI99KSkp2r59e71+7qWQ/FEnQ247qXseP6I3no/TfSk9deCLCD214oCi21UGOjTAayHlblVc0VJfTUio+XiFW+d6ttbxuzo3cGTwp4ur/e00SSosLNTp06et9uijj9Z4v6SkJGVmZmrdunVavHix8vPzNXjwYJ05c6bG84uLixUbG+uxLzY2VsXFxT797yAFOPlv3rxZI0eOVHx8vAzDUFZWViDDwSWMvee41q2I0d9Xxahgf4QWzOii8m8MpYw7EejQAK+dTYzW17fHq+yGNjUeP/PjdjoxupPOXhPZsIHBvy4+52+nSYqKivJotZX8U1NTdccdd6h///5KSUnR+++/r1OnTmn16tUN+atrFNDkX1ZWpsTERC1atCiQYeAymjV36+r+Z7VrS9VfhKZpaPeWSPUdSDkUAOqiTZs26tmzp3Jzc2s8HhcXp6NHj3rsO3r0aJ3XDHgjoMk/NTVVTz75pMaMGVOn88vLy1VSUuLR4H9RMS6FNpNOHfNcH3ryeDO17XA+QFEBgHd8Vfavr9LSUuXl5alTp041Hk9OTtaGDRs89q1fv17Jycn2blyDJjXnn5GR4bHIIiGh5vk6AACqaeDn/KdPn65Nmzbp4MGD2rZtm8aMGaPQ0FCNGzdOkjRhwgSP9QIPPvig1q1bp+eee0779u3TnDlztGPHDk2bNs3Or65Rk0r+jz76qMcii8LCwkCH5AglJ0LlOi+1+d4ov2378zp5jKdFAaAmhw8f1rhx49SrVy/deeedateunT7++GN16NBBklRQUKCioiLr/EGDBmnFihV6+eWXlZiYqL/85S/KysrStdde6/PYmtTf3Jd6lhL+c74yRPv/1VLX3XhG29dFS5IMw9SAG0u1JrNdgKMDgLpp6Hf7r1y58pLHN27cWG3fHXfcoTvuuMO7G9VDk0r+CJy3Xm6v6S8U6t+ftlTO7pYaM/WYIlq69feVMYEODfCacc6l5kfLre3mx8oVduis3K2a6Xz7MIWUnlezryvU7NSFR1nDis5JklzRzeVq0zwgMcMH+KqfheSPOtm0pq2i27k04eFite1wXgc+b6Hfju+mU8f5ixBNT0T+WXXJ2G9td1jxpSSp5MYYHb3nSrXafVpxrxyyjnf6w0FJ0tej43RibHyDxgr4Q0CTf2lpqccjD/n5+crOzlZMTIyuuOKKAEaGmqxZ1l5rlrUPdBiAbd/0idT+16+v9fiZwe10ZjBTWsGGT/pWCWjy37Fjh4YNG2Ztp6enS5LS0tKUmZkZoKgAAEGpHiv2q/UPEgFN/kOHDpUZRHMoAAA0Bcz5AwAcgbJ/FZI/AMAZ3OaFZqd/kCD5AwCcgTl/S5N6wx8AALCPkT8AwBEM2Zzz91kkgUfyBwA4A2/4s1D2BwDAYRj5AwAcgUf9qpD8AQDOwGp/C2V/AAAchpE/AMARDNOUYWPRnp2+jQ3JHwDgDO5vm53+QYKyPwAADsPIHwDgCJT9q5D8AQDOwGp/C8kfAOAMvOHPwpw/AAAOw8gfAOAIvOGvCskfAOAMlP0tlP0BAHAYRv4AAEcw3Beanf7BguQPAHAGyv4Wyv4AADgMI38AgDPwkh8LyR8A4Ai83rcKZX8AAByGkT8AwBlY8Gch+QMAnMGUZOdxveDJ/SR/AIAzMOdfhTl/AAAchpE/AMAZTNmc8/dZJAFH8gcAOAML/iyU/QEA8IOMjAz94Ac/UGRkpDp27KjRo0crJyfnkn0yMzNlGIZHi4iI8HlsJH8AgDO4fdC8sGnTJt133336+OOPtX79elVWVurWW29VWVnZJftFRUWpqKjIaocOHfLuxnVA2R8A4AgNvdp/3bp1HtuZmZnq2LGjdu7cqZtuuqn2+xiG4uLi6hVjXTHyBwDACyUlJR6tvLy8Tv1Onz4tSYqJibnkeaWlperatasSEhI0atQoff7557Zj/j6SPwDAGS4u+LPTJCUkJCg6OtpqGRkZl7212+3WQw89pB//+Me69tpraz2vV69eWrp0qd555x396U9/ktvt1qBBg3T48GGf/WeQKPsDAJzCR6v9CwsLFRUVZe0ODw+/bNf77rtPe/bs0datWy95XnJyspKTk63tQYMGqU+fPnrppZc0b968egZeHckfAAAvREVFeST/y5k2bZrWrl2rzZs3q0uXLl7dq3nz5rruuuuUm5vrbZiXRNkfAOAMPir71/12pqZNm6a3335bH374obp16+Z1yC6XS5999pk6derkdd9LYeQPAHAGtyTDZn8v3HfffVqxYoXeeecdRUZGqri4WJIUHR2tFi1aSJImTJigzp07W+sGnnjiCf3oRz9Sjx49dOrUKT377LM6dOiQpkyZYiPw6kj+AABHaOhH/RYvXixJGjp0qMf+ZcuWaeLEiZKkgoIChYRUFeFPnjypqVOnqri4WG3bttXAgQO1bds29e3bt95x14TkDwCAH5h1+MfCxo0bPbbnz5+v+fPn+ymiKiR/AIAz8G5/C8kfAOAMblMybCRwd/Akf1b7AwDgMIz8AQDOQNnfQvIHADiEzeSv4En+lP0BAHAYRv4AAGeg7G8h+QMAnMFtylbpntX+AACgqWLkDwBwBtN9odnpHyRI/gAAZ2DO30LyBwA4A3P+Fub8AQBwGEb+AABnoOxvIfkDAJzBlM3k77NIAo6yPwAADsPIHwDgDJT9LSR/AIAzuN2SbDyr7w6e5/wp+wMA4DCM/AEAzkDZ30LyBwA4A8nfQtkfAACHYeQPAHAGXu9rIfkDABzBNN0ybXyZz07fxobkDwBwBtO0N3pnzh8AADRVjPwBAM5g2pzzD6KRP8kfAOAMbrdk2Ji3D6I5f8r+AAA4DCN/AIAzUPa3kPwBAI5gut0ybZT9g+lRP8r+AAA4DCN/AIAzUPa3kPwBAM7gNiWD5C9R9gcAwHEY+QMAnME0Jdl5zj94Rv4kfwCAI5huU6aNsr9J8gcAoIkx3bI38udRPwAAUAeLFi3SlVdeqYiICCUlJemf//znJc9/88031bt3b0VERKhfv356//33fR4TyR8A4Aim27TdvLVq1Sqlp6fr8ccf165du5SYmKiUlBR99dVXNZ6/bds2jRs3TpMnT9bu3bs1evRojR49Wnv27LH78z2Q/AEAzmC67TcvPf/885o6daomTZqkvn37asmSJWrZsqWWLl1a4/kvvviiRowYoYcfflh9+vTRvHnzdP3112vhwoV2f72HJj3nf3HxxXlV2npvA9CYub85F+gQAL9xf1MuqWEW09nNFedVKUkqKSnx2B8eHq7w8PBq51dUVGjnzp169NFHrX0hISEaPny4tm/fXuM9tm/frvT0dI99KSkpysrKqn/gNWjSyf/MmTOSpK3y/XwI0Gjc806gIwD87syZM4qOjvbLtcPCwhQXF6etxfZzRevWrZWQkOCx7/HHH9ecOXOqnXv8+HG5XC7FxsZ67I+NjdW+fftqvH5xcXGN5xcXF9sL/HuadPKPj49XYWGhIiMjZRhGoMNxhJKSEiUkJKiwsFBRUVGBDgfwKf58NzzTNHXmzBnFx8f77R4RERHKz89XRUWF7WuZplkt39Q06m/smnTyDwkJUZcuXQIdhiNFRUXxlyOCFn++G5a/RvzfFRERoYiICL/f57vat2+v0NBQHT161GP/0aNHFRcXV2OfuLg4r86vLxb8AQDgB2FhYRo4cKA2bNhg7XO73dqwYYOSk5Nr7JOcnOxxviStX7++1vPrq0mP/AEAaMzS09OVlpamG264QT/84Q/1wgsvqKysTJMmTZIkTZgwQZ07d1ZGRoYk6cEHH9SQIUP03HPP6ac//alWrlypHTt26OWXX/ZpXCR/eCU8PFyPP/54k5zjAi6HP9/wtbvuukvHjh3T7NmzVVxcrAEDBmjdunXWor6CggKFhFQV4QcNGqQVK1boscce03/913/p6quvVlZWlq699lqfxmWYwfSyYgAAcFnM+QMA4DAkfwAAHIbkDwCAw5D8AQBwGJI/6szbz1ICTcXmzZs1cuRIxcfHyzAMn79HHWhsSP6oE28/Swk0JWVlZUpMTNSiRYsCHQrQIHjUD3WSlJSkH/zgB9ZnJd1utxISEnT//fdr5syZAY4O8B3DMPT2229r9OjRgQ4F8BtG/risi5+lHD58uLXvcp+lBAA0XiR/XNalPkvp689MAgD8j+QPAIDDkPxxWfX5LCUAoPEi+eOy6vNZSgBA48VX/VAnl/ssJdCUlZaWKjc319rOz89Xdna2YmJidMUVVwQwMsA/eNQPdbZw4UI9++yz1mcpFyxYoKSkpECHBdi2ceNGDRs2rNr+tLQ0ZWZmNnxAgJ+R/AEAcBjm/AEAcBiSPwAADkPyBwDAYUj+AAA4DMkfAACHIfkDAOAwJH8AAByG5A8AgMOQ/AGbJk6cqNGjR1vbQ4cO1UMPPdTgcWzcuFGGYejUqVO1nmMYhrKysup8zTlz5mjAgAG24jp48KAMw1B2drat6wDwHZI/gtLEiRNlGIYMw1BYWJh69OihJ554QufPn/f7vd966y3NmzevTufWJWEDgK/xYR8ErREjRmjZsmUqLy/X+++/r/vuu0/NmzfXo48+Wu3ciooKhYWF+eS+MTExPrkOAPgLI38ErfDwcMXFxalr16669957NXz4cK1Zs0ZSVan+qaeeUnx8vHr16iVJKiws1J133qk2bdooJiZGo0aN0sGDB61rulwupaenq02bNmrXrp0eeeQRff/zGN8v+5eXl2vGjBlKSEhQeHi4evTooddee00HDx60PibTtm1bGYahiRMnSrrwyeSMjAx169ZNLVq0UGJiov7yl7943Of9999Xz5491aJFCw0bNswjzrqaMWOGevbsqZYtW6p79+6aNWuWKisrq5330ksvKSEhQS1bttSdd96p06dPexx/9dVX1adPH0VERKh37976wx/+4HUsABoOyR+O0aJFC1VUVFjbGzZsUE5OjtavX6+1a9eqsrJSKSkpioyM1JYtW/SPf/xDrVu31ogRI6x+zz33nDIzM7V06VJt3bpVJ06c0Ntvv33J+06YMEF//vOftWDBAu3du1cvvfSSWrdurYSEBP31r3+VJOXk5KioqEgvvviiJCkjI0Ovv/66lixZos8//1y//vWv9Ytf/EKbNm2SdOEfKWPHjtXIkSOVnZ2tKVOmaObMmV7/N4mMjFRmZqa++OILvfjii3rllVc0f/58j3Nyc3O1evVqvfvuu1q3bp12796tX/3qV9bxN954Q7Nnz9ZTTz2lvXv36umnn9asWbO0fPlyr+MB0EBMIAilpaWZo0aNMk3TNN1ut7l+/XozPDzcnD59unU8NjbWLC8vt/r88Y9/NHv16mW63W5rX3l5udmiRQvzb3/7m2maptmpUyfzmWeesY5XVlaaXbp0se5lmqY5ZMgQ88EHHzRN0zRzcnJMSeb69etrjPOjjz4yJZknT5609p07d85s2bKluW3bNo9zJ0+ebI4bN840TdN89NFHzb59+3ocnzFjRrVrfZ8k8+233671+LPPPmsOHDjQ2n788cfN0NBQ8/Dhw9a+//3f/zVDQkLMoqIi0zRN86qrrjJXrFjhcZ158+aZycnJpmmaZn5+vinJ3L17d633BdCwmPNH0Fq7dq1at26tyspKud1u/cd//IfmzJljHe/Xr5/HPP+nn36q3NxcRUZGelzn3LlzysvL0+nTp1VUVKSkpCTrWLNmzXTDDTdUK/1flJ2drdDQUA0ZMqTOcefm5urs2bO65ZZbPPZXVFTouuuukyTt3bvXIw5JSk5OrvM9Llq1apUWLFigvLw8lZaW6vz584qKivI454orrlDnzp097uN2u5WTk6PIyEjl5eVp8uTJmjp1qnXO+fPnFR0d7XU8ABoGyR9Ba9iwYVq8eLHCwsIUHx+vZs08/7i3atXKY7u0tFQDBw7UG2+8Ue1aHTp0qFcMLVq08LpPaWmpJOm9997zSLrShXUMvrJ9+3aNHz9ec+fOVUpKiqKjo7Vy5Uo999xzXsf6yiuvVPvHSGhoqM9iBeBbJH8ErVatWqlHjx51Pv/666/XqlWr1LFjx2qj34s6deqkTz75RDfddJOkCyPcnTt36vrrr6/x/H79+sntdmvTpk0aPnx4teMXKw8ul8va17dvX4WHh6ugoKDWikGfPn2sxYsXffzxx5f/kd+xbds2de3aVb/97W+tfYcOHap2XkFBgY4cOaL4+HjrPiEhIerVq5diY2MVHx+vAwcOaPz48V7dH0DgsOAP+Nb48ePVvn17jRo1Slu2bFF+fr42btyoBx54QIcPH5YkPfjgg/rd736nrKws7du3T7/61a8u+Yz+lVdeqbS0NN19993Kysqyrrl69WpJUteuXWUYhtauXatjx46ptLRUkZGRmj59un79619r+fLlysvL065du/T73//eWkT3y1/+Uvv379fDDz+snJwcrVixQpmZmV793quvvloFBQVauXKl8vLytGDBghoXL0ZERCgtLU2ffvqptmzZogceeEB33nmn4uLiJElz585VRkaGFixYoH//+9/67LPPtGzZMj3//PNexQOg4ZD8gW+1bNlSmzdv1hVXXKGxY8eqT58+mjx5ss6dO2dVAn7zm9/oP//zP5WWlqbk5GRFRkZqzJgxl7zu4sWLdfvtt+tXv/qVevfuralTp6qsrEyS1LlzZ82dO1czZ85UbGyspk2bJkmaN2+eZs2apYyMDPXp00cjRozQe++9p27dukm6MA//17/+VVlZWUpMTNSSJUv09NNPe/V7b7vtNv3617/WtGnTNGDAAG3btk2zZs2qdl6PHj00duxY/eQnP9Gtt96q/v37ezzKN2XKFL366qtatmyZ+vXrpyFDhigzM9OKFUDjY5i1rVQCAABBiZE/AAAOQ/IHAMBhSP4AADgMyR8AAIch+QMA4DAkfwAAHIbkDwCAw5D8AQBwGJI/AAAOQ/IHAMBhSP4AADjM/weaS5sLoNazdAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay(confusion_matrix(target_val, my_predictions)).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Вывод:\n",
    "\n",
    "Качество модели, обученной с помощью класса MyLogisticRegression, получилось не хуже:\n",
    "- полнота = 1\n",
    "- точность = 1\n",
    "- F1-мера = 1"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}