Now that we've loaded the dataset, let's examine what is in it. The iris dataset pertains to a supervised classification problem.

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 types of flowers. Putting it all together, we have 150 examples of flowers with four measurements per flower in centimeters. For example, the first flower has measurements of 5.1 cm for sepal length, 3.5 cm for sepal width, 1.4 cm for petal length, and 0.2 cm for petal width. Now, let's look at the output variable in a similar manner:

iris.target

This yields an array of outputs: 0, 1, and 2. There are only three outputs. Type this:

iris.target.shape

You get a shape of:

(150L,)

This refers to an array of length 150 (150 x 1). Let's look at what the numbers refer to:

iris.target_names

array(['setosa', 'versicolor', 'virginica'], 
      dtype='|S10')

The output of the iris.target_names variable gives the English names for the numbers in the iris.target variable. The number zero corresponds to the setosa flower, number one corresponds to the versicolor flower, and number two corresponds to the virginica flower. Look at the first row of iris.target:

iris.target[0]

This produces zero, and thus the first row of observations we examined before correspond to the setosa flower.

In machine learning, we often deal with data tables and two-dimensional arrays corresponding to examples. In the iris set, we have 150 observations of flowers of three types. With new observations, we would like to predict which type of flower those observations correspond to. The observations in this case are measurements in centimeters. It is important to look at the data pertaining to real objects. Quoting my high school physics teacher, "Do not forget the units!"

The iris dataset is intended to be for a supervised machine learning task because it has a target array, which is the variable we desire to predict from the observation variables. Additionally, it is a classification problem, as there are three numbers we can predict from the observations, one for each type of flower. In a classification problem, we are trying to distinguish between categories. The simplest case is binary classification. The iris dataset, with three flower categories, is a multi-class classification problem.

With the same data, we can rephrase the problem in many ways, or formulate new problems. What if we want to determine relationships between the observations? We can define the petal width as the target variable. We can rephrase the problem as a regression problem and try to predict the target variable as a real number, not just three categories. Fundamentally, it comes down to what we intend to predict. Here, we desire to predict a type of flower.