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== Introduction == Decision trees are the most approachable and most fundamental sort of machine learned labelling algorithm. == Subject Matter Expert == [[User:jbm|Josh]] == Requirements == # Explain the idea behind a decision tree, including converting a set of decision criteria into a graphical representation # Describe at least three applications of decision trees # Discuss the strengths and weaknesses of decision trees # Discuss the appropriate inputs and outputs for a decision tree # 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 basic decision tree creation (all nominal values, no missing values) ## Demonstrate the creation of a decision tree by hand on a small dataset ## Demonstrate the creation of a decision tree on a larger dataset, using computer tools (off-the-shelf or custom) ## Explain the idea of pruning and its motivations # Demonstrate converting a set of criteria into executable code in any programming language, and validate with a test set == Resources == * [http://www.doc.ic.ac.uk/~sgc/teaching/v231/lecture11.html Decision Tree Learning] Another overview of decision trees, good for the entropy, gain, and manual creation steps * [http://www2.cs.uregina.ca/~dbd/cs831/notes/ml/dtrees/4_dtrees1.html Overview of Decision Trees] An overview of decision trees and their construction, at a fair bit of detail. * [http://www.autonlab.org/tutorials/dtree.html Andrew Moore's Decision Tree Slides], which offer a great review of the motivations and ideas of decision trees, but are a little terse.
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