Decision Tree Pruning

In machine learning and search algorithms, pruning is a data compression approach that minimizes the size of decision trees by deleting sections of the tree that are non-critical and redundant to classify instances. This reduces the amount of data that has to be stored in the tree. The prediction accuracy is improved as a result of the reduction in overfitting brought about by the use of pruning, which brings about a simplification of the final classifier.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Decision Tree Pruning

Chapter 2: Decision Tree Learning

Chapter 3: Data Compression

Chapter 4: Alpha-Beta Pruning

Chapter 5: Null-Move Heuristic

Chapter 6: Horizon Effect

Chapter 7: Minimum Description Length

Chapter 8: Bayesian Network

Chapter 9: Ensemble Learning

Chapter 10: Artificial Neural Network

(II) Answering the public top questions about decision tree pruning.

(III) Real world examples for the usage of decision tree pruning in many fields.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of decision tree pruning.

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