Decision Trees And Random Forests
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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
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*
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.
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.
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 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
Machine Learning With Random Forests And Decision Trees
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Author : Scott Hartshorn
language : en
Publisher:
Release Date : 2016
Machine Learning With Random Forests And Decision Trees written by Scott Hartshorn and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Algorithms categories.
Machine Learning
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Author :
language : en
Publisher:
Release Date : 2017
Machine Learning written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
"Learn intuitive machine learning techniques by exploring a classic problem. This Machine learning - decision trees and random forests online course will teach you cool machine learning techniques to predict survival probabilities aboard the Titanic - a Kaggle problem! In an age of decision fatigue and information overload, this course is a crisp yet thorough primer on two great machine learning techniques that help cut through the noise - decision trees and random forests."--Resource description page.
Advances In Knowledge Discovery And Management
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Author : Fabrice Guillet
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-06-11
Advances In Knowledge Discovery And Management written by Fabrice Guillet and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-11 with Computers categories.
During the last decade, the French-speaking scientific community developed a very strong research activity in the field of Knowledge Discovery and Management (KDM or EGC for “Extraction et Gestion des Connaissances” in French), which is concerned with, among others, Data Mining, Knowledge Discovery, Business Intelligence, Knowledge Engineering and SemanticWeb. The recent and novel research contributions collected in this book are extended and reworked versions of a selection of the best papers that were originally presented in French at the EGC 2009 Conference held in Strasbourg, France on January 2009. The volume is organized in four parts. Part I includes five papers concerned by various aspects of supervised learning or information retrieval. Part II presents five papers concerned with unsupervised learning issues. Part III includes two papers on data streaming and two on security while in Part IV the last four papers are concerned with ontologies and semantic.
Ai Meets Bi
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Author : Lakshman Bulusu
language : en
Publisher: CRC Press
Release Date : 2020-11-03
Ai Meets Bi written by Lakshman Bulusu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-03 with Computers categories.
With the emergence of Artificial Intelligence (AI) in the business world, a new era of Business Intelligence (BI) has been ushered in to create real-world business solutions using analytics. BI developers and practitioners now have tools and technologies to create systems and solutions to guide effective decision making. Decisions can be made on the basis of more reliable and accurate information and intelligence, which can lead to valuable, actionable insights for business. Previously, BI professionals were stymied by bad or incomplete data, poorly architected solutions, or even just outright incapable systems or resources. With the advent of AI, BI has new possibilities for effectiveness. This is a long-awaited phase for practitioners and developers and, moreover, for executives and leaders relying on knowledgeable and intelligent decision making for their organizations. Beginning with an outline of the traditional methods for implementing BI in the enterprise and how BI has evolved into using self-service analytics, data discovery, and most recently AI, AI Meets BI first lays out the three typical architectures of the first, second, and third generations of BI. It then takes an in-depth look at various types of analytics and highlights how each of these can be implemented using AI-enabled algorithms and deep learning models. The crux of the book is four industry use cases. They describe how an enterprise can access, assess, and perform analytics on data by way of discovering data, defining key metrics that enable the same, defining governance rules, and activating metadata for AI/ML recommendations. Explaining the implementation specifics of each of these four use cases by way of using various AI-enabled machine learning and deep learning algorithms, this book provides complete code for each of the implementations, along with the output of the code, supplemented by visuals that aid in BI-enabled decision making. Concluding with a brief discussion of the cognitive computing aspects of AI, the book looks at future trends, including augmented analytics, automated and autonomous BI, and security and governance of AI-powered BI.