machine-learning/sklearn-cross-validation.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Import the required modules"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cross_validation import train_test_split\n",
      "from sklearn.cross_validation import cross_val_score\n",
      "from sklearn.cross_validation import KFold\n",
      "from sklearn.pipeline import Pipeline\n",
      "from sklearn.preprocessing import StandardScaler\n",
      "from sklearn.linear_model import SGDClassifier\n",
      "from sklearn.datasets import load_iris\n",
      "from sklearn.metrics import classification_report\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Load the dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dataset = load_iris()\n",
      "X = dataset.data\n",
      "y = dataset.target"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Create the classification pipeline and calculate the scores"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf =  Pipeline([('scaler', StandardScaler()),('linear_model',\n",
      "    SGDClassifier())])\n",
      "cv = KFold(X.shape[0],5,shuffle=True, random_state=42)\n",
      "score = cross_val_score(clf, X, y, cv=cv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Print the mean score"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Mean = {}\".format(np.mean(score))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mean = 0.906666666667\n"
       ]
      }
     ],
     "prompt_number": 4
    }
   ],
   "metadata": {}
  }
 ]
}