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   "execution_count": 3,
   "id": "ffe61264-7fe9-49b2-b124-765ca3ebf76c",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        12\n",
      "           1       1.00      0.86      0.92         7\n",
      "           2       0.92      1.00      0.96        11\n",
      "\n",
      "    accuracy                           0.97        30\n",
      "   macro avg       0.97      0.95      0.96        30\n",
      "weighted avg       0.97      0.97      0.97        30\n",
      "\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",
    "\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)\n",
    "\n",
    "# Модель MLP — многослойный перцептрон\n",
    "clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=2500)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Отчёт о точности\n",
    "print(classification_report(y_test, clf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffb7a4d8-733b-4421-a91d-2cc73dc46b97",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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