FYS-STK4155/doc/Programs/JupyterFiles/Examples/Intro to ML Examples/Boston Housing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 104)\n"
     ]
    },
    {
     "data": {
      "image/png": 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EbiPyFxUe0taXG2umkDDnKW6smcKQtr4cjN3HgAEDrB2i1eTPKswvAGXrSuut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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bf1e5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set score: 0.771865\n",
      "test set score: 0.725968\n",
      "training set score: 0.771686\n",
      "test set score: 0.722415\n",
      "training set score: 0.771863\n",
      "test set score: 0.725626\n",
      "--------------------\n",
      "training set score: 0.771865\n",
      "test set score: 0.725916\n",
      "number of features used: 2\n"
     ]
    }
   ],
   "source": [
    "#!pip install mglearn\n",
    "import mglearn\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import IPython\n",
    "\n",
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()\n",
    "X, y = mglearn.datasets.load_extended_boston()\n",
    "print(X.shape)\n",
    "mglearn.plots.plot_knn_classification(n_neighbors=3)\n",
    "plt.show()\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X, y=mglearn.datasets.make_forge()\n",
    "\n",
    "X_train, X_test, y_train, y_test=train_test_split(X, y, random_state=0)\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "clf=KNeighborsClassifier(n_neighbors=3)\n",
    "clf.fit(X_train, y_train)\n",
    "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski')\n",
    "clf.predict(X_test)\n",
    "clf.score(X_test, y_test)\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr=LinearRegression().fit(X_train, y_train)\n",
    "\n",
    "print(\"training set score: %f\" % lr.score(X_train, y_train))\n",
    "print(\"test set score: %f\" % lr.score(X_test, y_test))\n",
    "\n",
    "from sklearn.linear_model import Ridge\n",
    "ridge = Ridge().fit(X_train, y_train)\n",
    "print(\"training set score: %f\" % ridge.score(X_train, y_train))\n",
    "print(\"test set score: %f\" % ridge.score(X_test, y_test))\n",
    "\n",
    "ridge01 = Ridge(alpha=0.1).fit(X_train, y_train)\n",
    "print(\"training set score: %f\" % ridge01.score(X_train, y_train))\n",
    "print(\"test set score: %f\" % ridge01.score(X_test, y_test))\n",
    "\n",
    "print (\"--------------------\")\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "lasso00001 = Lasso(alpha=0.0001).fit(X_train, y_train)\n",
    "print(\"training set score: %f\" % lasso00001.score(X_train, y_train))\n",
    "print(\"test set score: %f\" % lasso00001.score(X_test, y_test))\n",
    "print(\"number of features used: %d\" % np.sum(lasso00001.coef_ != 0))"
   ]
  },
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   },
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   "source": []
  }
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