MeritBadges/DecisionTrees
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Introduction
Decision trees are the most approachable and most fundamental sort of machine learned labelling algorithm.
Subject Matter Expert
Requirements
- Explain the idea behind a decision tree, including converting a set of decision criteria into a graphical representation
- Discuss the strengths and weaknesses of decision trees
- Explain fundamental machine learning concepts relevant to decision trees
- Explain the process of discretization of data
- Explain the causes of, and problems resulting from, an overfit model
- Describe the relationship between decision trees and entropy
- Demonstrate an understanding of information-theoretic entropy, including at least 3 computations by hand
- Explain information gain and how it relates to entropy
- Explain how entropy guides the learning of a decision tree
- Demonstrate decision tree creation
- Demonstrate the creation of a decision tree by hand on a small dataset (all nominal)
- Demonstrate the creation of a decision tree on a larger dataset, using computer tools (off-the-shelf or custom)
- Demonstrate converting a set of criteria into executable code in any programming language, and validate with a test set