Decision Forest

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Decision Tree And Random Forest Machine Learning And Algorithms

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Author : William Sullivan
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-03-06

Decision Tree And Random Forest Machine Learning And Algorithms written by William Sullivan and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-06 with categories.

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*

Decision Trees And Random Forests

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Author : Mark Koning
language : en
Publisher: Independently Published
Release Date : 2017-10-04

Decision Trees And Random Forests written by Mark Koning and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-04 with Computers categories.

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.

Random Forests With R

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Author : Robin Genuer
language : en
Publisher: Springer Nature
Release Date : 2020-09-10

Random Forests With R written by Robin Genuer and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-10 with Mathematics categories.

This book offers an application-oriented guide to random forests: a statistical learning method extensively used in many fields of application, thanks to its excellent predictive performance, but also to its flexibility, which places few restrictions on the nature of the data used. Indeed, random forests can be adapted to both supervised classification problems and regression problems. In addition, they allow us to consider qualitative and quantitative explanatory variables together, without pre-processing. Moreover, they can be used to process standard data for which the number of observations is higher than the number of variables, while also performing very well in the high dimensional case, where the number of variables is quite large in comparison to the number of observations. Consequently, they are now among the preferred methods in the toolbox of statisticians and data scientists. The book is primarily intended for students in academic fields such as statistical education, but also for practitioners in statistics and machine learning. A scientific undergraduate degree is quite sufficient to take full advantage of the concepts, methods, and tools discussed. In terms of computer science skills, little background knowledge is required, though an introduction to the R language is recommended. Random forests are part of the family of tree-based methods; accordingly, after an introductory chapter, Chapter 2 presents CART trees. The next three chapters are devoted to random forests. They focus on their presentation (Chapter 3), on the variable importance tool (Chapter 4), and on the variable selection problem (Chapter 5), respectively. After discussing the concepts and methods, we illustrate their implementation on a running example. Then, various complements are provided before examining additional examples. Throughout the book, each result is given together with the code (in R) that can be used to reproduce it. Thus, the book offers readers essential information and concepts, together with examples and the software tools needed to analyse data using random forests.

Growing A Tree From A Forest

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Author : Omer Sagi
language : en
Publisher:
Release Date : 2020

Growing A Tree From A Forest written by Omer Sagi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.

This research focuses on developing an algorithm that belongs to the latter category. It presents a set of methods for making decision forests more explainable, with the main contribution being a method for generating a decision tree from a pre-trained decision forest. Wecall the resulting model a forest based tree (FBT). It is generated by applying an algorithm that uses the inner structure of a decision forest to create a new intelligible model in the form of a single decision tree; this model is suitable for tasks that require explainable results. We first present a novel method for transforming independently trained decision forests (e.g.,random forest) into an interpretable decision tree which aims at preserving the predictive performance of the original forest.

Index Of The Decisions Of The Supreme Court Of Mauritius

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Author : Mauritius. Supreme Court
language : en
Publisher:
Release Date : 1884

Index Of The Decisions Of The Supreme Court Of Mauritius written by Mauritius. Supreme Court and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1884 with Law reports, digests, etc categories.

The Indian Law Reports

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Author :
language : en
Publisher:
Release Date : 1888

The Indian Law Reports written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1888 with Law categories.

Proceedings Of The Thirtieth Annual Acm Symposium On Theory Of Computing

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Author :
language : en
Publisher:
Release Date : 1998

Proceedings Of The Thirtieth Annual Acm Symposium On Theory Of Computing written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Computable functions categories.

Machine Learning

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Author : Christian Critelli
language : en
Publisher:
Release Date : 2021-03-03

Machine Learning written by Christian Critelli and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-03 with categories.

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

Introduction To Forest Resource Management

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Author : William A. Leuschner
language : en
Publisher:
Release Date : 1992

Introduction To Forest Resource Management written by William A. Leuschner and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Nature categories.

Forest Log

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Author :
language : en
Publisher:
Release Date : 1986-08

Forest Log written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986-08 with Forests and forestry categories.