feature_selection_new.ipynb

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   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "MAE: -26.581 (2.723)\n"
     ]
    }
   ],
   "source": [
    "from numpy import mean\n",
    "from numpy import std\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.pipeline import Pipeline\n",
    "# define dataset\n",
    "X, y = make_regression(n_samples=1000, n_features=10, n_informative=5, random_state=1)\n",
    "# create pipeline\n",
    "rfe = RFE(estimator=DecisionTreeRegressor(), n_features_to_select=5)\n",
    "model = DecisionTreeRegressor()\n",
    "pipeline = Pipeline(steps=[('s',rfe),('m',model)])\n",
    "# evaluate model\n",
    "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "n_scores = cross_val_score(pipeline, X, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1, error_score='raise')\n",
    "# report performance\n",
    "print('MAE: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Predicted: -84.288\n"
     ]
    }
   ],
   "source": [
    "# make a regression prediction with an RFE pipeline\n",
    "from numpy import mean\n",
    "from numpy import std\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.pipeline import Pipeline\n",
    "# define dataset\n",
    "X, y = make_regression(n_samples=1000, n_features=10, n_informative=5, random_state=1)\n",
    "# create pipeline\n",
    "rfe = RFE(estimator=DecisionTreeRegressor(), n_features_to_select=5)\n",
    "model = DecisionTreeRegressor()\n",
    "pipeline = Pipeline(steps=[('s',rfe),('m',model)])\n",
    "# fit the model on all available data\n",
    "pipeline.fit(X, y)\n",
    "# make a prediction for one example\n",
    "data = [[-2.02220122,0.31563495,0.82797464,-0.30620401,0.16003707,-1.44411381,0.87616892,-0.50446586,0.23009474,0.76201118]]\n",
    "yhat = pipeline.predict(data)\n",
    "print('Predicted: %.3f' % (yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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