03_getting_started_with_iris-checkpoint


Getting started in scikit-learn with the famous iris dataset

From the video series: Introduction to machine learning with scikit-learn

Agenda

  • What is the famous iris dataset, and how does it relate to machine learning?
  • How do we load the iris dataset into scikit-learn?
  • How do we describe a dataset using machine learning terminology?
  • What are scikit-learn's four key requirements for working with data?

Introducing the iris dataset

  • 50 samples of 3 different species of iris (150 samples total)
  • Measurements: sepal length, sepal width, petal length, petal width

In [2]:
from IPython.display import HTML
HTML('<iframe src=http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data width=300 height=200></iframe>')


Out[2]:

Machine learning on the iris dataset

  • Framed as a supervised learning problem: Predict the species of an iris using the measurements
  • Famous dataset for machine learning because prediction is easy
  • Learn more about the iris dataset: UCI Machine Learning Repository

Loading the iris dataset into scikit-learn


In [3]:
# import load_iris function from datasets module
from sklearn.datasets import load_iris

In [4]:
# save "bunch" object containing iris dataset and its attributes
iris = load_iris()
type(iris)


Out[4]:
sklearn.datasets.base.Bunch

In [5]:
# print the iris data
print iris.data


[[ 5.1  3.5  1.4  0.2]
 [ 4.9  3.   1.4  0.2]
 [ 4.7  3.2  1.3  0.2]
 [ 4.6  3.1  1.5  0.2]
 [ 5.   3.6  1.4  0.2]
 [ 5.4  3.9  1.7  0.4]
 [ 4.6  3.4  1.4  0.3]
 [ 5.   3.4  1.5  0.2]
 [ 4.4  2.9  1.4  0.2]
 [ 4.9  3.1  1.5  0.1]
 [ 5.4  3.7  1.5  0.2]
 [ 4.8  3.4  1.6  0.2]
 [ 4.8  3.   1.4  0.1]
 [ 4.3  3.   1.1  0.1]
 [ 5.8  4.   1.2  0.2]
 [ 5.7  4.4  1.5  0.4]
 [ 5.4  3.9  1.3  0.4]
 [ 5.1  3.5  1.4  0.3]
 [ 5.7  3.8  1.7  0.3]
 [ 5.1  3.8  1.5  0.3]
 [ 5.4  3.4  1.7  0.2]
 [ 5.1  3.7  1.5  0.4]
 [ 4.6  3.6  1.   0.2]
 [ 5.1  3.3  1.7  0.5]
 [ 4.8  3.4  1.9  0.2]
 [ 5.   3.   1.6  0.2]
 [ 5.   3.4  1.6  0.4]
 [ 5.2  3.5  1.5  0.2]
 [ 5.2  3.4  1.4  0.2]
 [ 4.7  3.2  1.6  0.2]
 [ 4.8  3.1  1.6  0.2]
 [ 5.4  3.4  1.5  0.4]
 [ 5.2  4.1  1.5  0.1]
 [ 5.5  4.2  1.4  0.2]
 [ 4.9  3.1  1.5  0.1]
 [ 5.   3.2  1.2  0.2]
 [ 5.5  3.5  1.3  0.2]
 [ 4.9  3.1  1.5  0.1]
 [ 4.4  3.   1.3  0.2]
 [ 5.1  3.4  1.5  0.2]
 [ 5.   3.5  1.3  0.3]
 [ 4.5  2.3  1.3  0.3]
 [ 4.4  3.2  1.3  0.2]
 [ 5.   3.5  1.6  0.6]
 [ 5.1  3.8  1.9  0.4]
 [ 4.8  3.   1.4  0.3]
 [ 5.1  3.8  1.6  0.2]
 [ 4.6  3.2  1.4  0.2]
 [ 5.3  3.7  1.5  0.2]
 [ 5.   3.3  1.4  0.2]
 [ 7.   3.2  4.7  1.4]
 [ 6.4  3.2  4.5  1.5]
 [ 6.9  3.1  4.9  1.5]
 [ 5.5  2.3  4.   1.3]
 [ 6.5  2.8  4.6  1.5]
 [ 5.7  2.8  4.5  1.3]
 [ 6.3  3.3  4.7  1.6]
 [ 4.9  2.4  3.3  1. ]
 [ 6.6  2.9  4.6  1.3]
 [ 5.2  2.7  3.9  1.4]
 [ 5.   2.   3.5  1. ]
 [ 5.9  3.   4.2  1.5]
 [ 6.   2.2  4.   1. ]
 [ 6.1  2.9  4.7  1.4]
 [ 5.6  2.9  3.6  1.3]
 [ 6.7  3.1  4.4  1.4]
 [ 5.6  3.   4.5  1.5]
 [ 5.8  2.7  4.1  1. ]
 [ 6.2  2.2  4.5  1.5]
 [ 5.6  2.5  3.9  1.1]
 [ 5.9  3.2  4.8  1.8]
 [ 6.1  2.8  4.   1.3]
 [ 6.3  2.5  4.9  1.5]
 [ 6.1  2.8  4.7  1.2]
 [ 6.4  2.9  4.3  1.3]
 [ 6.6  3.   4.4  1.4]
 [ 6.8  2.8  4.8  1.4]
 [ 6.7  3.   5.   1.7]
 [ 6.   2.9  4.5  1.5]
 [ 5.7  2.6  3.5  1. ]
 [ 5.5  2.4  3.8  1.1]
 [ 5.5  2.4  3.7  1. ]
 [ 5.8  2.7  3.9  1.2]
 [ 6.   2.7  5.1  1.6]
 [ 5.4  3.   4.5  1.5]
 [ 6.   3.4  4.5  1.6]
 [ 6.7  3.1  4.7  1.5]
 [ 6.3  2.3  4.4  1.3]
 [ 5.6  3.   4.1  1.3]
 [ 5.5  2.5  4.   1.3]
 [ 5.5  2.6  4.4  1.2]
 [ 6.1  3.   4.6  1.4]
 [ 5.8  2.6  4.   1.2]
 [ 5.   2.3  3.3  1. ]
 [ 5.6  2.7  4.2  1.3]
 [ 5.7  3.   4.2  1.2]
 [ 5.7  2.9  4.2  1.3]
 [ 6.2  2.9  4.3  1.3]
 [ 5.1  2.5  3.   1.1]
 [ 5.7  2.8  4.1  1.3]
 [ 6.3  3.3  6.   2.5]
 [ 5.8  2.7  5.1  1.9]
 [ 7.1  3.   5.9  2.1]
 [ 6.3  2.9  5.6  1.8]
 [ 6.5  3.   5.8  2.2]
 [ 7.6  3.   6.6  2.1]
 [ 4.9  2.5  4.5  1.7]
 [ 7.3  2.9  6.3  1.8]
 [ 6.7  2.5  5.8  1.8]
 [ 7.2  3.6  6.1  2.5]
 [ 6.5  3.2  5.1  2. ]
 [ 6.4  2.7  5.3  1.9]
 [ 6.8  3.   5.5  2.1]
 [ 5.7  2.5  5.   2. ]
 [ 5.8  2.8  5.1  2.4]
 [ 6.4  3.2  5.3  2.3]
 [ 6.5  3.   5.5  1.8]
 [ 7.7  3.8  6.7  2.2]
 [ 7.7  2.6  6.9  2.3]
 [ 6.   2.2  5.   1.5]
 [ 6.9  3.2  5.7  2.3]
 [ 5.6  2.8  4.9  2. ]
 [ 7.7  2.8  6.7  2. ]
 [ 6.3  2.7  4.9  1.8]
 [ 6.7  3.3  5.7  2.1]
 [ 7.2  3.2  6.   1.8]
 [ 6.2  2.8  4.8  1.8]
 [ 6.1  3.   4.9  1.8]
 [ 6.4  2.8  5.6  2.1]
 [ 7.2  3.   5.8  1.6]
 [ 7.4  2.8  6.1  1.9]
 [ 7.9  3.8  6.4  2. ]
 [ 6.4  2.8  5.6  2.2]
 [ 6.3  2.8  5.1  1.5]
 [ 6.1  2.6  5.6  1.4]
 [ 7.7  3.   6.1  2.3]
 [ 6.3  3.4  5.6  2.4]
 [ 6.4  3.1  5.5  1.8]
 [ 6.   3.   4.8  1.8]
 [ 6.9  3.1  5.4  2.1]
 [ 6.7  3.1  5.6  2.4]
 [ 6.9  3.1  5.1  2.3]
 [ 5.8  2.7  5.1  1.9]
 [ 6.8  3.2  5.9  2.3]
 [ 6.7  3.3  5.7  2.5]
 [ 6.7  3.   5.2  2.3]
 [ 6.3  2.5  5.   1.9]
 [ 6.5  3.   5.2  2. ]
 [ 6.2  3.4  5.4  2.3]
 [ 5.9  3.   5.1  1.8]]

Machine learning terminology

  • Each row is an observation (also known as: sample, example, instance, record)
  • Each column is a feature (also known as: predictor, attribute, independent variable, input, regressor, covariate)

In [6]:
# print the names of the four features
print iris.feature_names


['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

In [7]:
# print integers representing the species of each observation
print iris.target


[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
 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
 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
 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
 2 2]

In [8]:
# print the encoding scheme for species: 0 = setosa, 1 = versicolor, 2 = virginica
print iris.target_names


['setosa' 'versicolor' 'virginica']
  • Each value we are predicting is the response (also known as: target, outcome, label, dependent variable)
  • Classification is supervised learning in which the response is categorical
  • Regression is supervised learning in which the response is ordered and continuous

Requirements for working with data in scikit-learn

  1. Features and response are separate objects
  2. Features and response should be numeric
  3. Features and response should be NumPy arrays
  4. Features and response should have specific shapes

In [9]:
# check the types of the features and response
print type(iris.data)
print type(iris.target)


<type 'numpy.ndarray'>
<type 'numpy.ndarray'>

In [10]:
# check the shape of the features (first dimension = number of observations, second dimensions = number of features)
print iris.data.shape


(150L, 4L)

In [11]:
# check the shape of the response (single dimension matching the number of observations)
print iris.target.shape


(150L,)

In [12]:
# store feature matrix in "X"
X = iris.data

# store response vector in "y"
y = iris.target

Resources

Comments or Questions?


In [1]:
from IPython.core.display import HTML
def css_styling():
    styles = open("styles/custom.css", "r").read()
    return HTML(styles)
css_styling()


Out[1]: