Getting started with the famous Iris dataset
Getting started with the famous Iris dataset

Topics

  1. About Iris dataset
  2. Display Iris dataset
  3. Supervised learning on Iris dataset
  4. Loading the Iris dataset into scikit-learn
  5. Machine learning terminology
  6. Exploring the Iris dataset
  7. Requirements for working with datasets in scikit-learn
  8. Additional resources

This tutorial is derived from Data School's Machine Learning with scikit-learn tutorial. I added my own notes so anyone, including myself, can refer to this tutorial without watching the videos.

1. About Iris dataset

Iris

  • The iris dataset contains the following data
    • 50 samples of 3 different species of iris (150 samples total)
    • Measurements: sepal length, sepal width, petal length, petal width
  • The format for the data: (sepal length, sepal width, petal length, petal width)

2. Display Iris Dataset

In [1]:
# Display HTML using IPython.display module
# You can display any other HTML using this module too
# Just replace the link with your desired HTML page
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[1]:

3. Supervised 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

4. Loading the iris dataset into scikit-learn

In [2]:
# import load_iris function from datasets module
# convention is to import modules instead of sklearn as a whole
from sklearn.datasets import load_iris
In [3]:
# save "bunch" object containing iris dataset and its attributes
# the data type is "bunch"
iris = load_iris()
type(iris)
Out[3]:
sklearn.datasets.base.Bunch
In [5]:
# print the iris data
# same data as shown previously
# each row represents each sample
# each column represents the features
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]]

5. 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)

6. Exploring the iris dataset

In [ ]:
# print the names of the four features
print iris.feature_names
In [ ]:
# print integers representing the species of each observation
# 0, 1, and 2 represent different species
print iris.target
In [ ]:
# print the encoding scheme for species: 0 = setosa, 1 = versicolor, 2 = virginica
print iris.target_names
  • 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
    • "0": setosa
    • "1": versicolor
    • "2": virginica
  • Regression is supervised learning in which the response is ordered and continuous
    • any number (continuous)

7. Requirements for working with data in scikit-learn

  1. Features and response are separate objects
    • In this case, data and target are separate
  2. Features and response should be numeric
    • In this case, features and response are numeric with the matrix dimension of 150 x 4
  3. Features and response should be NumPy arrays
    • The iris dataset contains NumPy arrays already
    • For other dataset, by loading them into NumPy
  4. Features and response should have specific shapes
    • 150 x 4 for whole dataset
    • 150 x 1 for examples
    • 4 x 1 for features
    • you can convert the matrix accordingly using np.tile(a, [4, 1]), where a is the matrix and [4, 1] is the intended matrix dimensionality
In [6]:
# check the types of the features and response
print(type(iris.data))
print(type(iris.target))

<class 'numpy.ndarray'>
<class 'numpy.ndarray'>
In [8]:
# check the shape of the features (first dimension = number of observations, second dimensions = number of features)
print(iris.data.shape)

(150, 4)
In [7]:
# check the shape of the response (single dimension matching the number of observations)
print(iris.target.shape)

(150,)
In [ ]:
# store feature matrix in "X"
X = iris.data

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

8. Resources

Tags: machine_learning