Задание 4.ipynb

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   "source": [
    "import numpy as np\n",
    "import sklearn\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
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   "cell_type": "code",
   "execution_count": 19,
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   "source": [
    "dataset = load_boston()\n",
    "X, y = dataset['data'], dataset['target']\n",
    "X = scale(X)"
   ]
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   "cell_type": "code",
   "execution_count": 26,
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    "ret = []\n",
    "kf = KFold(n_splits=5, random_state=42, shuffle=True)\n",
    "for p in np.linspace(1, 10, 200):\n",
    "    clf = KNeighborsRegressor(n_neighbors=5, weights='distance', p=p)\n",
    "    score = cross_val_score(estimator=clf, X=X, y=y, scoring='neg_mean_squared_error', cv=kf)\n",
    "    ret.append((p, score.mean()))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 27,
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       "(1.0, -16.050208508436157)"
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     "execution_count": 27,
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   "source": [
    "max(ret, key=lambda i: i[1])"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "source": []
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