Using Trees for Predictive Analysis | Artificial Intelligence and Machine Learning Fundamentals

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Artificial Intelligence and Machine Learning Fundamentals

By : Nagy

4.3 (110)

Buy this Book

Artificial Intelligence and Machine Learning Fundamentals

4.3 (110)

By: Nagy

Buy this Book

Overview of this book

Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!

Preface

About the Book

Free Chapter

Principles of Artificial Intelligence

Introduction

Fields and Applications of Artificial Intelligence

AI Tools and Learning Models

The Role of Python in Artificial Intelligence

Python for Game AI

Summary

AI with Search Techniques and Games

Introduction

Heuristics

Pathfinding with the A* Algorithm

Game AI with the Minmax Algorithm and Alpha-Beta Pruning

Summary

Regression

Introduction

Linear Regression with One Variable

Linear Regression with Multiple Variables

Polynomial and Support Vector Regression

Summary

Classification

Introduction

The Fundamentals of Classification

Classification with Support Vector Machines

Summary

Using Trees for Predictive Analysis

Introduction to Decision Trees

Random Forest Classifier

Summary

Clustering

Introduction to Clustering

The k-means Algorithm

Mean Shift Algorithm

Summary

Deep Learning with Neural Networks

Introduction

TensorFlow for Python

Introduction to Neural Networks

Deep Learning

Summary

Appendix

Chapter 1: Principles of AI

Chapter 2: AI with Search Techniques and Games

Chapter 4: Classification

Chapter 5: Using Trees for Predictive Analysis

Chapter 6: Clustering

Chapter 7: Deep Learning with Neural Networks

Random Forest Classifier

If you think about the name Random forest classifier, it makes sense to conclude the following:

A forest consists of multiple trees.
These trees can be used for classification.
Since the only tree we have used so far for classification is a decision tree, it makes sense that the random forest is a forest of decision trees.
The random nature of the trees means that our decision trees are constructed in a randomized manner.
As a consequence, we will base our decision tree construction on information gain or Gini Impurity.

Once you understand these basic concepts, you essentially know what a Random forest classifier is all about. The more trees you have in the forest, the more accurate prediction is going to be. When performing prediction, each tree performs classification. We collect the results, and the class that gets the most votes wins.

Random forests can be used for regression as well as for classification. When using random...

Artificial Intelligence and Machine Learning Fundamentals

By : Nagy

Artificial Intelligence and Machine Learning Fundamentals

By: Nagy

Overview of this book

Random Forest Classifier

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