Задание 12.ipynb

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   "source": [
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('abalone.csv')\n",
    "data['Sex'] = data['Sex'].map(lambda x: 1 if x == 'M' else (-1 if x == 'F' else 0))\n",
    "X, y = data.iloc[:, :-1], data.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "kf = KFold(n_splits=5, shuffle=True, random_state=1)\n",
    "scores = {}\n",
    "for n in range(1, 51):\n",
    "    clf = RandomForestRegressor(n_estimators=n)\n",
    "    for train_index, test_index in kf.split(X, y):\n",
    "        pass\n",
    "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "        clf.fit(X_train, y_train)\n",
    "        scores[n] = r2_score(y_test, clf.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
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       " 48: 0.52151759721856994,\n",
       " 49: 0.53450573397137757,\n",
       " 50: 0.53767918499986045}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('answer.txt', 'w') as f:\n",
    "    f.write('26')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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