From cb320c71a8fb27bbe25a1752d57e110ddc83a39f Mon Sep 17 00:00:00 2001 From: Navneet Singh Date: Mon, 11 Jul 2022 14:58:55 +0530 Subject: [PATCH] added Bayesian Ridge Regressor --- IME672_project/Proj.ipynb | 104 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 104 insertions(+) diff --git a/IME672_project/Proj.ipynb b/IME672_project/Proj.ipynb index 4444552..a693f09 100644 --- a/IME672_project/Proj.ipynb +++ b/IME672_project/Proj.ipynb @@ -3313,6 +3313,13 @@ "ridge_regressor.fit(x_train,y_train)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 57, @@ -5641,6 +5648,103 @@ "print(\"\\nRMSE:\\n\",rmse_test,rmse_train)" ] }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
BayesianRidge()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "BayesianRidge()" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import BayesianRidge\n", + "br_reg = BayesianRidge()\n", + "br_reg.fit(x_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1668.20495034, 1679.94492734, 913.64922033, ..., 1282.17224146,\n", + " 473.76584417, 1836.95624818])" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "br_reg.predict(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.14911786e+03, 1.79137805e+02, 1.58422799e+03, -1.76336580e+03,\n", + " -2.06966485e+02, -9.64991490e+01, -3.59451467e+01, 1.20811300e+03,\n", + " 4.63318397e+02, -1.17801277e+02, -1.73995862e+02, -1.05291009e+02,\n", + " -1.45517075e+02, -4.08851312e+02, 1.73110503e+03, -1.37393040e+02,\n", + " -1.22026370e+02, -1.03060886e+02, -1.19399700e+02, -1.78063514e+02,\n", + " -1.16492327e+02, 2.76553693e-10, -1.45549124e+02, -3.52070971e+01,\n", + " -1.81655358e+02, -1.12378682e+02, -1.05109365e+02, -6.32679934e+01,\n", + " -1.20354808e+02, -1.21063182e+02, 2.35545128e+00, -1.03861844e+02,\n", + " -1.06205028e+02, -1.42506556e+02, -1.08923435e+02, 8.49374765e+00])" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "br_reg.coef_" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RMSE:\n", + " 490.458 491.25\n" + ] + } + ], + "source": [ + "y_pred_train = br_reg.predict(x_train)\n", + "y_pred = br_reg.predict(x_test)\n", + "rmse_test = float(format(np.sqrt(mean_squared_error(y_test, y_pred)),'.3f'))\n", + "rmse_train = float(format(np.sqrt(mean_squared_error(y_train, y_pred_train)),'.3f'))\n", + "print(\"\\nRMSE:\\n\",rmse_test,rmse_train)" + ] + }, { "cell_type": "code", "execution_count": null,