Editing MeritBadges/DecisionTrees
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# Explain the idea behind a decision tree, including converting a set of decision criteria into a graphical representation | # 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 | # Discuss the strengths and weaknesses of decision trees | ||
# Explain fundamental machine learning concepts relevant to decision trees | # Explain fundamental machine learning concepts relevant to decision trees | ||
## Explain the process of discretization of data | ## Explain the process of discretization of data | ||
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## Explain information gain and how it relates to entropy | ## Explain information gain and how it relates to entropy | ||
## Explain how entropy guides the learning of a decision tree | ## Explain how entropy guides the learning of a decision tree | ||
# Demonstrate | # Demonstrate decision tree creation | ||
## Demonstrate the creation of a decision tree by hand on a small dataset | ## 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 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 | # Demonstrate converting a set of criteria into executable code in any programming language, and validate with a test set | ||
== Resources == | == Resources == | ||