{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#MULTI CLASS CLASSIFICATION"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Data Imports\n",
"import numpy as np\n",
"import pandas as pd\n",
"from pandas import Series,DataFrame\n",
"\n",
"# Plot imports\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"sns.set_style('whitegrid')\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn import linear_model\n",
"from sklearn.datasets import load_iris"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"iris = load_iris()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X = iris.data\n",
"\n",
"Y = iris.target"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Iris Plants Database\n",
"\n",
"Notes\n",
"-----\n",
"Data Set Characteristics:\n",
" :Number of Instances: 150 (50 in each of three classes)\n",
" :Number of Attributes: 4 numeric, predictive attributes and the class\n",
" :Attribute Information:\n",
" - sepal length in cm\n",
" - sepal width in cm\n",
" - petal length in cm\n",
" - petal width in cm\n",
" - class:\n",
" - Iris-Setosa\n",
" - Iris-Versicolour\n",
" - Iris-Virginica\n",
" :Summary Statistics:\n",
" ============== ==== ==== ======= ===== ====================\n",
" Min Max Mean SD Class Correlation\n",
" ============== ==== ==== ======= ===== ====================\n",
" sepal length: 4.3 7.9 5.84 0.83 0.7826\n",
" sepal width: 2.0 4.4 3.05 0.43 -0.4194\n",
" petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n",
" petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n",
" ============== ==== ==== ======= ===== ====================\n",
" :Missing Attribute Values: None\n",
" :Class Distribution: 33.3% for each of 3 classes.\n",
" :Creator: R.A. Fisher\n",
" :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
" :Date: July, 1988\n",
"\n",
"This is a copy of UCI ML iris datasets.\n",
"http://archive.ics.uci.edu/ml/datasets/Iris\n",
"\n",
"The famous Iris database, first used by Sir R.A Fisher\n",
"\n",
"This is perhaps the best known database to be found in the\n",
"pattern recognition literature. Fisher's paper is a classic in the field and\n",
"is referenced frequently to this day. (See Duda & Hart, for example.) The\n",
"data set contains 3 classes of 50 instances each, where each class refers to a\n",
"type of iris plant. One class is linearly separable from the other 2; the\n",
"latter are NOT linearly separable from each other.\n",
"\n",
"References\n",
"----------\n",
" - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
" Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
" Mathematical Statistics\" (John Wiley, NY, 1950).\n",
" - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
" (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n",
" - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
" Structure and Classification Rule for Recognition in Partially Exposed\n",
" Environments\". IEEE Transactions on Pattern Analysis and Machine\n",
" Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
" - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\n",
" on Information Theory, May 1972, 431-433.\n",
" - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n",
" conceptual clustering system finds 3 classes in the data.\n",
" - Many, many more ...\n",
"\n"
]
}
],
"source": [
"print iris.DESCR"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"iris_data = DataFrame(X,columns=['Sepal Length','Sepal Width','Petal Length','Petal Width'])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"iris_target = DataFrame(Y,columns=['Species'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def flower(num):\n",
" if num == 0:\n",
" return 'Setosa'\n",
" elif num == 1:\n",
" return 'Versicolour'\n",
" else:\n",
" return 'Virginica'"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"iris_target['Species'] = iris_target['Species'].apply(flower)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Species | \n",
"
\n",
" \n",
" \n",
" \n",
" | 145 | \n",
" Virginica | \n",
"
\n",
" \n",
" | 146 | \n",
" Virginica | \n",
"
\n",
" \n",
" | 147 | \n",
" Virginica | \n",
"
\n",
" \n",
" | 148 | \n",
" Virginica | \n",
"
\n",
" \n",
" | 149 | \n",
" Virginica | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" Sepal Length | \n",
" Sepal Width | \n",
" Petal Length | \n",
" Petal Width | \n",
" Species | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 5.1 | \n",
" 3.5 | \n",
" 1.4 | \n",
" 0.2 | \n",
" Setosa | \n",
"
\n",
" \n",
" | 1 | \n",
" 4.9 | \n",
" 3.0 | \n",
" 1.4 | \n",
" 0.2 | \n",
" Setosa | \n",
"
\n",
" \n",
" | 2 | \n",
" 4.7 | \n",
" 3.2 | \n",
" 1.3 | \n",
" 0.2 | \n",
" Setosa | \n",
"
\n",
" \n",
" | 3 | \n",
" 4.6 | \n",
" 3.1 | \n",
" 1.5 | \n",
" 0.2 | \n",
" Setosa | \n",
"
\n",
" \n",
" | 4 | \n",
" 5.0 | \n",
" 3.6 | \n",
" 1.4 | \n",
" 0.2 | \n",
" Setosa | \n",
"
\n",
" \n",
"
\n",
"