lab_4/lab4.ipynb

@@ -166,7 +166,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Конвертация данных:"
+    "### Предобработка данных:"
    ]
   },
   {
@@ -1130,7 +1130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 323,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1177,10 +1177,10 @@
     "\n",
     "\n",
     "# Оценка качества различных моделей на основе метрик\n",
-    "def evaluate_models(models, \n",
-    "                    pipeline_end, \n",
-    "                    X_train, y_train, \n",
-    "                    X_test, y_test) -> dict[str, dict[str, Any]]:\n",
+    "def evaluate_models(models: dict[str, Any], \n",
+    "                    pipeline_end: Pipeline, \n",
+    "                    X_train: DataFrame, y_train, \n",
+    "                    X_test: DataFrame, y_test) -> dict[str, dict[str, Any]]:\n",
     "    results: dict[str, dict[str, Any]] = {}\n",
     "    \n",
     "    for model_name, model in models.items():\n",
@@ -1298,7 +1298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 325,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1368,6 +1368,7 @@
     "# Меняем знак, так как берем отрицательное значение MSE\n",
     "new_best_mse = -new_grid_search.best_score_\n",
     "\n",
+    "\n",
     "# Обучение модели с лучшими параметрами для новых значений\n",
     "model_best = RandomForestRegressor(**new_best_params)\n",
     "model_best.fit(X_train_processing_result, y_train)\n",
@@ -1379,6 +1380,7 @@
     "mse = metrics.mean_squared_error(y_test, y_pred)\n",
     "rmse = np.sqrt(mse)\n",
     "\n",
+    "\n",
     "# Вывод результатов\n",
     "print(\"Старые параметры:\", old_best_params)\n",
     "print(\"Лучший результат (MSE) на старых параметрах:\", old_best_mse)\n",