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     "output_type": "stream",
     "text": [
      "==================================================\n",
      "MLPClassifier\n",
      "==================================================\n",
      "\n",
      "1. max_iter = 100 (недообучение)\n",
      "   Точность на тесте: 0.4000\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        10\n",
      "           1       0.00      0.00      0.00         9\n",
      "           2       0.18      0.18      0.18        11\n",
      "\n",
      "    accuracy                           0.40        30\n",
      "   macro avg       0.39      0.39      0.39        30\n",
      "weighted avg       0.40      0.40      0.40        30\n",
      "\n",
      "\n",
      "2. max_iter = 500 (оптимальное)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Пользователь\\PycharmProjects\\PythonProject7\\week4-feature-selection\\venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:785: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Пользователь\\PycharmProjects\\PythonProject7\\week4-feature-selection\\venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:785: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Точность на тесте: 0.9333\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        10\n",
      "           1       1.00      0.78      0.88         9\n",
      "           2       0.85      1.00      0.92        11\n",
      "\n",
      "    accuracy                           0.93        30\n",
      "   macro avg       0.95      0.93      0.93        30\n",
      "weighted avg       0.94      0.93      0.93        30\n",
      "\n",
      "\n",
      "3. max_iter = 2500 (долгое обучение)\n",
      "   Точность на тесте: 0.9667\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        10\n",
      "           1       1.00      0.89      0.94         9\n",
      "           2       0.92      1.00      0.96        11\n",
      "\n",
      "    accuracy                           0.97        30\n",
      "   macro avg       0.97      0.96      0.97        30\n",
      "weighted avg       0.97      0.97      0.97        30\n",
      "\n",
      "\n",
      "==================================================\n",
      "ВЫВОДЫ\n",
      "==================================================\n",
      "max_iter=100  → точность: 0.4000 (недообучение)\n",
      "max_iter=500  → точность: 0.9333 (оптимально)\n",
      "max_iter=2500 → точность: 0.9667 (могло переобучиться)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "import numpy as np\n",
    "\n",
    "# Загрузка и разбиение данных\n",
    "X, y = load_iris(return_X_y=True)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "print(\"=\"*50)\n",
    "print(\"MLPClassifier\")\n",
    "print(\"=\"*50)\n",
    "\n",
    "# max_iter = 100 (недообучение)\n",
    "print(\"\\n1. max_iter = 100 (недообучение)\")\n",
    "clf_100 = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=100, random_state=42)\n",
    "clf_100.fit(X_train, y_train)\n",
    "print(f\"   Точность на тесте: {clf_100.score(X_test, y_test):.4f}\")\n",
    "print(classification_report(y_test, clf_100.predict(X_test), zero_division=0))\n",
    "\n",
    "# max_iter = 500 (оптимальное)\n",
    "print(\"\\n2. max_iter = 500 (оптимальное)\")\n",
    "clf_500 = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=500, random_state=42)\n",
    "clf_500.fit(X_train, y_train)\n",
    "print(f\"   Точность на тесте: {clf_500.score(X_test, y_test):.4f}\")\n",
    "print(classification_report(y_test, clf_500.predict(X_test)))\n",
    "\n",
    "# max_iter = 2500 (переобучение)\n",
    "print(\"\\n3. max_iter = 2500 (долгое обучение)\")\n",
    "clf_2500 = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=2500, random_state=42)\n",
    "clf_2500.fit(X_train, y_train)\n",
    "print(f\"   Точность на тесте: {clf_2500.score(X_test, y_test):.4f}\")\n",
    "print(classification_report(y_test, clf_2500.predict(X_test)))\n",
    "\n",
    "# Вывод\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"ВЫВОДЫ\")\n",
    "print(\"=\"*50)\n",
    "print(f\"max_iter=100  → точность: {clf_100.score(X_test, y_test):.4f} (недообучение)\")\n",
    "print(f\"max_iter=500  → точность: {clf_500.score(X_test, y_test):.4f} (оптимально)\")\n",
    "print(f\"max_iter=2500 → точность: {clf_2500.score(X_test, y_test):.4f} (могло переобучиться)\")"
   ]
  },
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   "cell_type": "code",
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "5ccb37ef-fb03-406c-8017-d0b74a00ea38",
   "metadata": {},
   "outputs": [],
   "source": []
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