Machine Learning for OpenCV 4 - Second Edition

Splitting data into training and test sets

We learned, in the previous chapter, that it is essential to keep training and test data separate. We can easily split the data using one of scikit-learn's many helper functions:

In [11]: X_train, X_test, y_train, y_test = model_selection.train_test_split(...            data, target, test_size=0.1, random_state=42...      )

Here, we want to split the data into 90% training data and 10% test data, which we specify with test_size=0.1. By inspecting the return arguments, we note that we ended up with exactly 90 training data points and 10 test data points:

In [12]: X_train.shape, y_train.shapeOut[12]: ((90, 4), (90,))In [13]: X_test.shape, y_test.shapeOut[13]: ((10, 4), (10,))