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
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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 basic decision tree creation (all nominal values, no missing values)
## Demonstrate the creation of a decision tree by hand on a small dataset (all nominal)
## 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]

Josh

Requirements[edit]

  1. Explain the idea behind a decision tree, including converting a set of decision criteria into a graphical representation
  2. Describe at least three applications of decision trees
  3. Discuss the strengths and weaknesses of decision trees
  4. Discuss the appropriate inputs and outputs for a decision tree
  5. Explain fundamental machine learning concepts relevant to decision trees
    1. Explain the process of discretization of data
    2. Explain the causes of, and problems resulting from, an overfit model
  6. Describe the relationship between decision trees and entropy
    1. Demonstrate an understanding of information-theoretic entropy, including at least 3 computations by hand
    2. Explain information gain and how it relates to entropy
    3. Explain how entropy guides the learning of a decision tree
  7. Demonstrate basic decision tree creation (all nominal values, no missing values)
    1. Demonstrate the creation of a decision tree by hand on a small dataset
    2. Demonstrate the creation of a decision tree on a larger dataset, using computer tools (off-the-shelf or custom)
    3. Explain the idea of pruning and its motivations
  8. Demonstrate converting a set of criteria into executable code in any programming language, and validate with a test set

Resources[edit]