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Test-Driven Machine Learning
book

Test-Driven Machine Learning

by Justin Bozonier
November 2015
Intermediate to advanced content levelIntermediate to advanced
190 pages
4h 11m
English
Packt Publishing
Content preview from Test-Driven Machine Learning

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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Publisher Resources

ISBN: 9781784399085Supplemental Content