Decision Tree And Random Forest Machine Learning And Algorithms
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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*
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
Tree Based Machine Learning Algorithms
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Author : Clinton Sheppard
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-09-09
Tree Based Machine Learning Algorithms written by Clinton Sheppard 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 2017-09-09 with Decision trees categories.
"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.
Machine Learning For Beginners Book
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Author : Casimira Youngberg
language : en
Publisher: Independently Published
Release Date : 2021-07-09
Machine Learning For Beginners Book written by Casimira Youngberg and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-09 with categories.
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
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.
Machine Learning For Beginners Guide Algorithms
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Author : William Sullivan
language : en
Publisher: PublishDrive
Release Date : 2017-08-19
Machine Learning For Beginners Guide Algorithms written by William Sullivan and has been published by PublishDrive this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-19 with Computers categories.
Machines can LEARN ?!?! Machine learning occurs primarily through the use of " algorithms" and other elaborate procedures Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning Note: Bonus chapters included inside! Instead of spending hundreds or even thousands of dollars on courses/materials why not read this book instead? Its a worthwhile read and the most valuable investment you can make for yourself 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 for beginners. What You'll Learn Supervised Learning Unsupervised Learning Reinforced Learning Algorithms Decision Tree Random Forest Neural Networks Python Deep Learning And much, much more! This is the most comprehensive and easy to read step by step guide in machine learning that exists. Learn from one of the most reliable programmers alive and expert in the field You do not want to miss out on this incredible offer!
Data Mining With Decision Trees
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Author : Lior Rokach
language : en
Publisher: World Scientific
Release Date : 2008
Data Mining With Decision Trees written by Lior Rokach and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computers categories.
This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique.Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition. This book invites readers to explore the many benefits in data mining that decision trees offer: Self-explanatory and easy to follow when compacted Able to handle a variety of input data: nominal, numeric and textual Able to process datasets that may have errors or missing values High predictive performance for a relatively small computational effort Available in many data mining packages over a variety of platforms Useful for various tasks, such as classification, regression, clustering and feature selection
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.
Meta Learning In Decision Tree Induction
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Author : Krzysztof Grąbczewski
language : en
Publisher: Springer
Release Date : 2013-09-11
Meta Learning In Decision Tree Induction written by Krzysztof Grąbczewski and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-11 with Technology & Engineering categories.
The book focuses on different variants of decision tree induction but also describes the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimental methodology and evaluation framework is provided. Meta-learning is discussed in great detail in the second half of the book. The exposition starts by presenting a comprehensive review of many meta-learning approaches explored in the past described in literature, including for instance approaches that provide a ranking of algorithms. The approach described can be related to other work that exploits planning whose aim is to construct data mining workflows. The book stimulates interchange of ideas between different, albeit related, approaches.
Computational Intelligence Data Analytics And Applications
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Author : Fausto Pedro García Márquez
language : en
Publisher: Springer Nature
Release Date : 2023-03-14
Computational Intelligence Data Analytics And Applications written by Fausto Pedro García Márquez and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-14 with Technology & Engineering categories.
This book is a compilation of accepted papers presented at the International Conference on Computing, Intelligence and Data Analytics (ICCIDA) in 2022 organized by Information Systems Engineering of the Kocaeli University, Turkey on September 16-17, 2022. The book highlights some of the latest research advances and cutting-edge analyses of real-world problems related to Computing, Intelligence and Data Analytics and their applications in various domains. This includes state of the art models and methods used on benchmark datasets.