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# Developing testable documentation

In this part of the chapter, we'll just explore different classifier algorithms, and learn the ins and outs of each.

## Decision trees

Let's start with decision trees. scikit-learn has some great documentation, which you can find at http://scikit-learn.org/stable/. So, let's jump over there, and look up an example that states how to use their decision tree. The following is a test with the details greatly simplified to get to the simplest possible example:

`from sklearn.tree import DecisionTreeRegressor def decision_tree_can_predict_perfect_linear_relationship_test(): decision_tree = DecisionTreeRegressor() decision_tree.fit([[1],[1.1],[2]], [[0],[0],[1]]) predicted_value = decision_tree.predict([[-1],[5]]) assert list(predicted_value) ...`

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