MeritBadges/DecisionTrees: Difference between revisions
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(New page: == Introduction == Decision trees are the most approachable and most fundamental sort of machine learned labelling algorithm. == Subject Matter Expert == Josh == Requireme...) |
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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 | |||
# 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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# Describe the relationship between decision trees and entropy | # Describe the relationship between decision trees and entropy | ||
## Demonstrate an understanding of information-theoretic entropy, including at least 3 computations by hand | ## 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 | ## Explain how entropy guides the learning of a decision tree | ||
# Demonstrate decision tree creation | # Demonstrate decision tree creation | ||
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## 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 == | |||
Revision as of 13:00, 11 January 2009
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