Supervised Learning: Algorithms and Business Use Cases II

Decision trees

These are highly interpretable models that can be used for both classification and regression tasks. They split data-feature values into branches at decision nodes (e.g, if a feature is a color, each possible color becomes a new branch) until a final decision output is made. Decision trees are often fast and accurate and, hence, are widely used.

Business use cases

• Understanding product attributes that make a product most likely to be purchased

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