scikit-learn Cookbook - Second Edition

How to do it...

1. To access the observation variables, type:

iris.data

This outputs a NumPy array:

array([[ 5.1,  3.5,  1.4,  0.2],       [ 4.9,  3. ,  1.4,  0.2],       [ 4.7,  3.2,  1.3,  0.2], #...rest of output suppressed because of length

2. Let's examine the NumPy array:

iris.data.shape

This returns:

(150L, 4L)

This means that the data is 150 rows by 4 columns. Let's look at the first row:

iris.data[0]array([ 5.1,  3.5,  1.4,  0.2])

The NumPy array for the first row has four numbers.

3. To determine what they mean, type:

iris.feature_names['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

The feature or column names name the data. They are strings, and in this case, they correspond to dimensions in different ...