Loading the training data

The Iris dataset is included with scikit-learn. We first load all the necessary modules, as we did in our earlier examples:

In [1]: import numpy as np
...     import cv2
...     from sklearn import datasets
...     from sklearn import model_selection
...     from sklearn import metrics
...     import matplotlib.pyplot as plt
...     %matplotlib inline
In [2]: plt.style.use('ggplot')

Then, loading the dataset is a one-liner:

In [3]: iris = datasets.load_iris()

This function returns a dictionary we call iris, which contains a bunch of different fields:

In [4]: dir(iris)
Out[4]: ['DESCR', 'data', 'feature_names', 'target', 'target_names']

Here, all the data points are contained in 'data'. There are 150 data points, each of which have four feature values:

In [5]: iris.data.shape
Out[5]: (150, 4)

These four features correspond to the sepal and petal dimensions mentioned earlier:

In [6]: iris.feature_names
Out[6]: ['sepal length (cm)',
         'sepal width (cm)',
         'petal length (cm)',
         'petal width (cm)']

For every data point, we have a class label stored in target:

In [7]: iris.target.shape
Out[7]: (150,)

We can also inspect the class labels, and find that there is a total of three classes:

In [8]: np.unique(iris.target)
Out[8]: array([0, 1, 2])