Module Learning Outcomes

Module Learning Outcomes

By the end of the module, students are expected to:

• Broadly describe how decision trees make predictions.
• Use DecisionTreeClassifier() and DecisionTreeRegressor() to build decision trees using scikit-learn.
• Explain the .fit() and .predict() paradigm and use .score() method of ML models.
• Explain the concept of decision boundaries.
• Explain the difference between parameters and hyperparameters.
• Explain how decision boundaries change with max_depth.
• Explain the concept of generalization.

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