Learning Data Mining with Python - Second Edition

Moving towards a standard workflow

Estimators scikit-learn have two and predict(). We train the algorithm using the predict() method on our testing set. We evaluate it using the predict() method on our testing set.

1. First, we need to create these training and testing sets. As before, import and run the train_test_split function:

from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=14)

2. Then, we import the nearest neighbor class and create an instance for it. We leave the parameters as defaults for now and will test other values later in this chapter. By default, the algorithm will choose the five nearest neighbors to predict the class of a testing sample:

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