Ridge_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_regression\n",
    "x,y = make_regression(n_features=1,noise=5,n_samples=10000) # making a regression datasets.\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x,y,s=2) # s is scale\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, Y_train, X_test, Y_test = train_test_split(x,y,test_size=0.3,random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [7000, 3000]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[39], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinear_model\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Ridge\n\u001b[0;32m      2\u001b[0m reg \u001b[38;5;241m=\u001b[39m Ridge()\n\u001b[1;32m----> 3\u001b[0m \u001b[43mreg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43mY_train\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\user\\miniconda3\\lib\\site-packages\\sklearn\\base.py:1152\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1145\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1147\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1148\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1149\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1150\u001b[0m     )\n\u001b[0;32m   1151\u001b[0m ):\n\u001b[1;32m-> 1152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\user\\miniconda3\\lib\\site-packages\\sklearn\\linear_model\\_ridge.py:1123\u001b[0m, in \u001b[0;36mRidge.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m   1103\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Fit Ridge regression model.\u001b[39;00m\n\u001b[0;32m   1104\u001b[0m \n\u001b[0;32m   1105\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1120\u001b[0m \u001b[38;5;124;03m    Fitted estimator.\u001b[39;00m\n\u001b[0;32m   1121\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1122\u001b[0m _accept_sparse \u001b[38;5;241m=\u001b[39m _get_valid_accept_sparse(sparse\u001b[38;5;241m.\u001b[39missparse(X), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msolver)\n\u001b[1;32m-> 1123\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1124\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1125\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1126\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_accept_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1127\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat64\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1128\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmulti_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m   1129\u001b[0m \u001b[43m    \u001b[49m\u001b[43my_numeric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m   1130\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1131\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mfit(X, y, sample_weight\u001b[38;5;241m=\u001b[39msample_weight)\n",
      "File \u001b[1;32mc:\\Users\\user\\miniconda3\\lib\\site-packages\\sklearn\\base.py:622\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[0;32m    620\u001b[0m         y \u001b[38;5;241m=\u001b[39m check_array(y, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params)\n\u001b[0;32m    621\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 622\u001b[0m         X, y \u001b[38;5;241m=\u001b[39m check_X_y(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n\u001b[0;32m    623\u001b[0m     out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[0;32m    625\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n",
      "File \u001b[1;32mc:\\Users\\user\\miniconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1164\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m   1146\u001b[0m X \u001b[38;5;241m=\u001b[39m check_array(\n\u001b[0;32m   1147\u001b[0m     X,\n\u001b[0;32m   1148\u001b[0m     accept_sparse\u001b[38;5;241m=\u001b[39maccept_sparse,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1159\u001b[0m     input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1160\u001b[0m )\n\u001b[0;32m   1162\u001b[0m y \u001b[38;5;241m=\u001b[39m _check_y(y, multi_output\u001b[38;5;241m=\u001b[39mmulti_output, y_numeric\u001b[38;5;241m=\u001b[39my_numeric, estimator\u001b[38;5;241m=\u001b[39mestimator)\n\u001b[1;32m-> 1164\u001b[0m \u001b[43mcheck_consistent_length\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1166\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m X, y\n",
      "File \u001b[1;32mc:\\Users\\user\\miniconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:407\u001b[0m, in \u001b[0;36mcheck_consistent_length\u001b[1;34m(*arrays)\u001b[0m\n\u001b[0;32m    405\u001b[0m uniques \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39munique(lengths)\n\u001b[0;32m    406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(uniques) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 407\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    408\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound input variables with inconsistent numbers of samples: \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    409\u001b[0m         \u001b[38;5;241m%\u001b[39m [\u001b[38;5;28mint\u001b[39m(l) \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m lengths]\n\u001b[0;32m    410\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [7000, 3000]"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "reg = Ridge()\n",
    "reg.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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