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 | ||
# 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 fundamental machine learning concepts relevant to decision trees | ||
## Explain the process of discretization of data | ## Explain the process of discretization of data | ||
Line 15: | Line 18: | ||
# 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 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 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) | ## 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 | # 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. |
Latest revision as of 13:12, 11 January 2009
Introduction[edit]
Decision trees are the most approachable and most fundamental sort of machine learned labelling algorithm.
Subject Matter Expert[edit]
Requirements[edit]
- 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[edit]
- Decision Tree Learning Another overview of decision trees, good for the entropy, gain, and manual creation steps
- Overview of Decision Trees An overview of decision trees and their construction, at a fair bit of detail.
- Andrew Moore's Decision Tree Slides, which offer a great review of the motivations and ideas of decision trees, but are a little terse.