{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open in Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/justmarkham/scikit-learn-tips/master?filepath=notebooks%2F23_linear_model_coefficients.ipynb)\n",
    "\n",
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/justmarkham/scikit-learn-tips/blob/master/notebooks/23_linear_model_coefficients.ipynb)\n",
    "\n",
    "# 🤖⚡ scikit-learn tip #23 ([video](https://www.youtube.com/watch?v=JmYR283vCdw&list=PL5-da3qGB5ID7YYAqireYEew2mWVvgmj6&index=23))\n",
    "\n",
    "Q: How do you display the intercept & coefficients for a linear model?\n",
    "\n",
    "A: They are stored as attributes of the model: `intercept_` and `coef_`\n",
    "\n",
    "Attributes end with `_` if they are set during \"fit\"\n",
    "\n",
    "See example 👇"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "dataset = load_diabetes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = dataset.data, dataset.target\n",
    "features = dataset.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    "model.fit(X, y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152.1334841628965"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -10.01219782, -239.81908937,  519.83978679,  324.39042769,\n",
       "       -792.18416163,  476.74583782,  101.04457032,  177.06417623,\n",
       "        751.27932109,   67.62538639])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('age', -10.012197817470962),\n",
       " ('sex', -239.81908936565566),\n",
       " ('bmi', 519.8397867901349),\n",
       " ('bp', 324.39042768937657),\n",
       " ('s1', -792.1841616283053),\n",
       " ('s2', 476.7458378236622),\n",
       " ('s3', 101.04457032134493),\n",
       " ('s4', 177.06417623225025),\n",
       " ('s5', 751.2793210873947),\n",
       " ('s6', 67.62538639104369)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the feature names with the coefficients\n",
    "list(zip(features, model.coef_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Want more tips? [View all tips on GitHub](https://github.com/justmarkham/scikit-learn-tips) or [Sign up to receive 2 tips by email every week](https://scikit-learn.tips) 💌\n",
    "\n",
    "© 2020 [Data School](https://www.dataschool.io). All rights reserved."
   ]
  }
 ],
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