Now, we will split the data into training and test sets.
The iris dataset is divided into the following parts:
- data
- target
- target_names
- DESCR
- feature_names, and other details
We are mostly interested in the data (which contains the samples divided into 4 features), and the target (which is the target variable, and contains the classes of flowers).
Here, the classes of flowers are 'setosa', 'versicolor', and 'virginica'. The features includes 'sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', and 'petal width (cm)'.
Load the data part of the dataset into X variable, and target part into y variable
<< your code goes here >> = iris.data
<< your code goes here >> = iris.target
Next, split the X and y variables into training and test sets using the train_test_split function in a 70/30 ratio
X_train, X_test, y_train, y_test = train_test_split(<< your code goes here >>, y, test_size=<< your code goes here >>, random_state=42)
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