{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simple classification example: the iris dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from sklearn import datasets\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.metrics import confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# import some data to play with\n",
"# columns: Sepal Length, Sepal Width, Petal Length and Petal Width\n",
"iris = datasets.load_iris()\n",
"X = iris.data\n",
"y = iris.target"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
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"
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" \n",
" \n",
" | \n",
" Sepal Length (cm) | \n",
" Sepal Width (cm) | \n",
" Petal Length (cm) | \n",
" Petal Width (cm) | \n",
" category | \n",
"
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" \n",
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" | 0 | \n",
" 5.1 | \n",
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" 4.9 | \n",
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" | 3 | \n",
" 4.6 | \n",
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" | 4 | \n",
" 5.0 | \n",
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"