Issues in Decision Tree Learning



Issues in Decision Tree Learning

  • Determining How Deeply To Grow The Decision Tree,

  • Handling Continuous Attributes,

  • Choosing An Appropriate Attribute Selection Measure,

  • Handling Training Data With Missing Attribute Values,

  • Handling Attributes With Differing Costs, And

  • Improving Computational Efficiency.

Understanding Hypothesis

  • A hypothesis is a function that best describes the target in supervised machine learning.

  • A hypothesis is a function that best describes the target in supervised machine learning.

A few examples:

  • "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."

  • "Students who experience test anxiety prior to an English exam will get higher scores than students who do not experience test anxiety."

  • "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."