Alternating Decision Tree

A categorization strategy that may be learned by machine learning is known as an alternating decision tree, or ADTree. It is connected to boosting and generalizes decision trees at the same time.

How You Will Benefit

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

Chapter 1: Alternating Decision Tree

Chapter 2: Decision Tree Learning

Chapter 3: AdaBoost

Chapter 4: Random Forest

Chapter 5: Gradient Boosting

Chapter 6: Propositional Calculus

Chapter 7: Support Vector Machine

Chapter 8: Method of Analytic Tableaux

Chapter 9: Boolean Satisfiability Algorithm Heuristics

Chapter 10: Multiplicative Weight Update Method

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

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

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of alternating decision tree' technologies.

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 alternating decision tree.