"Learn how to use decision trees and random forests for classification and regression, their respective limitations, and how the algorithms that build them work. Each chapter introduces a new data concern and then walks you through modifying the code, thus building the engine just-in-time. Along the way you will gain experience making decision trees and random forests work for you."--Back cover.

Decision Tree And Random Forest: Artificial Intelligence Series Decision Tree and Random Forest have real world applications using algorithms These are behind many fundamental activities, services and processes we humans take for granted! We interact with these "behind the scene" processes on a daily basis without even knowing! This book installment goes over the fundamental concepts of both Decision Trees and Random Forests, but explains it to readers in more simple terms and breaks down the complexity of the subject matter in more comprehensible components. What You'll Learn... Structure of Decision Tree What Constitutes Random Forests Algorithms Recursive Binary Splitting Regression Vs Classification Trees K-NN ( K-nearest neighbor) Deep learning Aspects of Bayes' Theorem And.. Much, Much More! Other books easily retail for $50-$100+ and have far less quality content. This book is by far superior and exceeds any other book available. High quality diagrams included, visual aids have been proven to help accelerate the learning process 110% times faster than texts alone. Make the greatest investment in yourself by investing in your knowledge! Buy Now *Note: For the best visual experience of diagrams it is highly recommend you purchase the paperback version*

This book gathers selected papers presented at the International Conference on Machine Intelligence and Soft Computing (ICMISC 2020), held jointly by Vignan’s Institute of Information Technology, Visakhapatnam, India and VFSTR Deemed to be University, Guntur, AP, India during 03-04 September 2020. Topics covered in the book include the artificial neural networks and fuzzy logic, cloud computing, evolutionary algorithms and computation, machine learning, metaheuristics and swarm intelligence, neuro-fuzzy system, soft computing and decision support systems, soft computing applications in actuarial science, soft computing for database deadlock resolution, soft computing methods in engineering, and support vector machine.

If you want to learn how decision trees and random forests work, plus create your own, this visual book is for you. The fact is, decision tree and random forest algorithms are powerful and likely touch your life everyday. From online search to product development and credit scoring, both types of algorithms are at work behind the scenes in many modern applications and services. They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own algorithms in Python, this book is for you.

If you want to learn how decision trees and random forests work, plus create your own, this Machine Learning Algorithms visual book is for you. The topics covered in this Machine Learning Algorithms book are: - An overview of decision trees and random forests - A manual example of how a human would classify a dataset, compared to how a decision tree would work - How a decision tree works, and why it is prone to overfitting - How decision trees get combined to form a random forest - How to use that random forest to classify data and make predictions - How to determine how many trees to use in a random forest - Just where does the "randomness" come from - Out of Bag Errors & Cross-Validation - how good of a fit did the machine learning algorithm make? - Gini Criteria & Entropy Criteria - how to tell which split on a decision tree is best among many possible choices - And More

The aim of this book is to provide a thorough introduction to various aspects of trees in random settings and a systematic treatment of the mathematical analysis techniques involved. It should serve as a reference book as well as a basis for future research.

Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. If you are someone who learns by playing with the code and editing the data or equations to see what changes, then use those resources along with the book for a deeper understanding. The topics covered in this book are: -An overview of decision trees and random forests -A manual example of how a human would classify a dataset, compared to how a decision tree would work -How a decision tree works, and why it is prone to overfitting -How decision trees get combined to form a random forest -How to use that random forest to classify data and make predictions -How to determine how many trees to use in a random forest -Just where does the "randomness" come from -Out of Bag Errors & Cross-Validation - how good of a fit did the machine learning algorithm make? -Gini Criteria & Entropy Criteria - how to tell which split on a decision tree is best among many possible choices -And More