{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Getting started in scikit-learn with the famous iris dataset ([video #3](https://www.youtube.com/watch?v=hd1W4CyPX58&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=3))\n",
"\n",
"Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n",
"\n",
"**Note:** This notebook uses Python 3.9.1 and scikit-learn 0.23.2. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Agenda\n",
"\n",
"- What is the famous iris dataset, and how does it relate to Machine Learning?\n",
"- How do we load the iris dataset into scikit-learn?\n",
"- How do we describe a dataset using Machine Learning terminology?\n",
"- What are scikit-learn's four key requirements for working with data?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introducing the iris dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Iris](images/03_iris.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- 50 samples of 3 different species of iris (150 samples total)\n",
"- Measurements: sepal length, sepal width, petal length, petal width"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# added empty cell so that the cell numbering matches the video"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import IFrame\n",
"IFrame('https://www.dataschool.io/files/iris.txt', width=300, height=200)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Machine Learning on the iris dataset\n",
"\n",
"- Framed as a **supervised learning** problem: Predict the species of an iris using the measurements\n",
"- Famous dataset for Machine Learning because prediction is **easy**\n",
"- Learn more about the iris dataset: [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/datasets/Iris)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading the iris dataset into scikit-learn"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# import load_iris function from datasets module\n",
"from sklearn.datasets import load_iris"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"sklearn.utils.Bunch"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# save \"bunch\" object containing iris dataset and its attributes\n",
"iris = load_iris()\n",
"type(iris)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[5.1 3.5 1.4 0.2]\n",
" [4.9 3. 1.4 0.2]\n",
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" [5. 3.6 1.4 0.2]\n",
" [5.4 3.9 1.7 0.4]\n",
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" [5. 3.4 1.5 0.2]\n",
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" [4.5 2.3 1.3 0.3]\n",
" [4.4 3.2 1.3 0.2]\n",
" [5. 3.5 1.6 0.6]\n",
" [5.1 3.8 1.9 0.4]\n",
" [4.8 3. 1.4 0.3]\n",
" [5.1 3.8 1.6 0.2]\n",
" [4.6 3.2 1.4 0.2]\n",
" [5.3 3.7 1.5 0.2]\n",
" [5. 3.3 1.4 0.2]\n",
" [7. 3.2 4.7 1.4]\n",
" [6.4 3.2 4.5 1.5]\n",
" [6.9 3.1 4.9 1.5]\n",
" [5.5 2.3 4. 1.3]\n",
" [6.5 2.8 4.6 1.5]\n",
" [5.7 2.8 4.5 1.3]\n",
" [6.3 3.3 4.7 1.6]\n",
" [4.9 2.4 3.3 1. ]\n",
" [6.6 2.9 4.6 1.3]\n",
" [5.2 2.7 3.9 1.4]\n",
" [5. 2. 3.5 1. ]\n",
" [5.9 3. 4.2 1.5]\n",
" [6. 2.2 4. 1. ]\n",
" [6.1 2.9 4.7 1.4]\n",
" [5.6 2.9 3.6 1.3]\n",
" [6.7 3.1 4.4 1.4]\n",
" [5.6 3. 4.5 1.5]\n",
" [5.8 2.7 4.1 1. ]\n",
" [6.2 2.2 4.5 1.5]\n",
" [5.6 2.5 3.9 1.1]\n",
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" [6.8 2.8 4.8 1.4]\n",
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" [5.7 2.6 3.5 1. ]\n",
" [5.5 2.4 3.8 1.1]\n",
" [5.5 2.4 3.7 1. ]\n",
" [5.8 2.7 3.9 1.2]\n",
" [6. 2.7 5.1 1.6]\n",
" [5.4 3. 4.5 1.5]\n",
" [6. 3.4 4.5 1.6]\n",
" [6.7 3.1 4.7 1.5]\n",
" [6.3 2.3 4.4 1.3]\n",
" [5.6 3. 4.1 1.3]\n",
" [5.5 2.5 4. 1.3]\n",
" [5.5 2.6 4.4 1.2]\n",
" [6.1 3. 4.6 1.4]\n",
" [5.8 2.6 4. 1.2]\n",
" [5. 2.3 3.3 1. ]\n",
" [5.6 2.7 4.2 1.3]\n",
" [5.7 3. 4.2 1.2]\n",
" [5.7 2.9 4.2 1.3]\n",
" [6.2 2.9 4.3 1.3]\n",
" [5.1 2.5 3. 1.1]\n",
" [5.7 2.8 4.1 1.3]\n",
" [6.3 3.3 6. 2.5]\n",
" [5.8 2.7 5.1 1.9]\n",
" [7.1 3. 5.9 2.1]\n",
" [6.3 2.9 5.6 1.8]\n",
" [6.5 3. 5.8 2.2]\n",
" [7.6 3. 6.6 2.1]\n",
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" [7.3 2.9 6.3 1.8]\n",
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" [7.2 3.6 6.1 2.5]\n",
" [6.5 3.2 5.1 2. ]\n",
" [6.4 2.7 5.3 1.9]\n",
" [6.8 3. 5.5 2.1]\n",
" [5.7 2.5 5. 2. ]\n",
" [5.8 2.8 5.1 2.4]\n",
" [6.4 3.2 5.3 2.3]\n",
" [6.5 3. 5.5 1.8]\n",
" [7.7 3.8 6.7 2.2]\n",
" [7.7 2.6 6.9 2.3]\n",
" [6. 2.2 5. 1.5]\n",
" [6.9 3.2 5.7 2.3]\n",
" [5.6 2.8 4.9 2. ]\n",
" [7.7 2.8 6.7 2. ]\n",
" [6.3 2.7 4.9 1.8]\n",
" [6.7 3.3 5.7 2.1]\n",
" [7.2 3.2 6. 1.8]\n",
" [6.2 2.8 4.8 1.8]\n",
" [6.1 3. 4.9 1.8]\n",
" [6.4 2.8 5.6 2.1]\n",
" [7.2 3. 5.8 1.6]\n",
" [7.4 2.8 6.1 1.9]\n",
" [7.9 3.8 6.4 2. ]\n",
" [6.4 2.8 5.6 2.2]\n",
" [6.3 2.8 5.1 1.5]\n",
" [6.1 2.6 5.6 1.4]\n",
" [7.7 3. 6.1 2.3]\n",
" [6.3 3.4 5.6 2.4]\n",
" [6.4 3.1 5.5 1.8]\n",
" [6. 3. 4.8 1.8]\n",
" [6.9 3.1 5.4 2.1]\n",
" [6.7 3.1 5.6 2.4]\n",
" [6.9 3.1 5.1 2.3]\n",
" [5.8 2.7 5.1 1.9]\n",
" [6.8 3.2 5.9 2.3]\n",
" [6.7 3.3 5.7 2.5]\n",
" [6.7 3. 5.2 2.3]\n",
" [6.3 2.5 5. 1.9]\n",
" [6.5 3. 5.2 2. ]\n",
" [6.2 3.4 5.4 2.3]\n",
" [5.9 3. 5.1 1.8]]\n"
]
}
],
"source": [
"# print the iris data\n",
"print(iris.data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Machine Learning terminology\n",
"\n",
"- Each row is an **observation** (also known as: sample, example, instance, record)\n",
"- Each column is a **feature** (also known as: predictor, attribute, independent variable, input, regressor, covariate)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
]
}
],
"source": [
"# print the names of the four features\n",
"print(iris.feature_names)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2]\n"
]
}
],
"source": [
"# print integers representing the species of each observation\n",
"print(iris.target)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['setosa' 'versicolor' 'virginica']\n"
]
}
],
"source": [
"# print the encoding scheme for species: 0 = setosa, 1 = versicolor, 2 = virginica\n",
"print(iris.target_names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Each value we are predicting is the **response** (also known as: target, outcome, label, dependent variable)\n",
"- **Classification** is supervised learning in which the response is categorical\n",
"- **Regression** is supervised learning in which the response is ordered and continuous"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Requirements for working with data in scikit-learn\n",
"\n",
"1. Features and response are **separate objects**\n",
"2. Features should always be **numeric**, and response should be **numeric** for regression problems\n",
"3. Features and response should be **NumPy arrays**\n",
"4. Features and response should have **specific shapes**"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n"
]
}
],
"source": [
"# check the types of the features and response\n",
"print(type(iris.data))\n",
"print(type(iris.target))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(150, 4)\n"
]
}
],
"source": [
"# check the shape of the features (first dimension = number of observations, second dimensions = number of features)\n",
"print(iris.data.shape)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(150,)\n"
]
}
],
"source": [
"# check the shape of the response (single dimension matching the number of observations)\n",
"print(iris.target.shape)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# store feature matrix in \"X\"\n",
"X = iris.data\n",
"\n",
"# store response vector in \"y\"\n",
"y = iris.target"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Resources\n",
"\n",
"- scikit-learn documentation: [Dataset loading utilities](https://scikit-learn.org/stable/datasets.html)\n",
"- Jake VanderPlas: Fast Numerical Computing with NumPy ([slides](https://speakerdeck.com/jakevdp/losing-your-loops-fast-numerical-computing-with-numpy-pycon-2015), [video](https://www.youtube.com/watch?v=EEUXKG97YRw))\n",
"- Scott Shell: [An Introduction to NumPy](https://sites.engineering.ucsb.edu/~shell/che210d/numpy.pdf) (PDF)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comments or Questions?\n",
"\n",
"- Email: \n",
"- Website: https://www.dataschool.io\n",
"- Twitter: [@justmarkham](https://twitter.com/justmarkham)\n",
"\n",
"© 2021 [Data School](https://www.dataschool.io). All rights reserved."
]
}
],
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