Random Forests
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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.
Random Forests
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Author : Yu. L. Pavlov
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2019-01-14
Random Forests written by Yu. L. Pavlov and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-14 with Mathematics categories.
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Handbook Of Random Forests Theory And Applications For Remote Sensing
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Author : Ronny Hansch
language : en
Publisher: World Scientific Publishing Company
Release Date : 2024
Handbook Of Random Forests Theory And Applications For Remote Sensing written by Ronny Hansch and has been published by World Scientific Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Computers categories.
This compendium provides a hands-on description of random forests. The text starts with a consistent introduction of general methods to create, train, and fuse ensembles of decision trees. Instead of limiting the explanation to the general-purpose layout of traditional random forests, this book outlines specifications during tree creation and training, that are especially well suited to analyze structured data such as images. The theoretical foundations are explained as deeply as practical and implementation issues. The many possible variations of the underlying Random Forest model are discussed as well as their implications on the outcome in order to provide insights into the influence of these parameters and their possible side-effects. Last but not least, this unique title provides specific examples of the usage of Random Forests for analysis tasks of remote sensing imagery.
Topics In Random Forests
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Author : Chao Chen
language : en
Publisher:
Release Date : 2005
Topics In Random Forests written by Chao Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
Tensorflow Machine Learning Projects
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Author : Ankit Jain
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-11-30
Tensorflow Machine Learning Projects written by Ankit Jain and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-30 with Computers categories.
Implement TensorFlow's offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects Key FeaturesUse machine learning and deep learning principles to build real-world projectsGet to grips with TensorFlow's impressive range of module offeringsImplement projects on GANs, reinforcement learning, and capsule networkBook Description TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem. To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification. As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts. By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work. What you will learnUnderstand the TensorFlow ecosystem using various datasets and techniquesCreate recommendation systems for quality product recommendationsBuild projects using CNNs, NLP, and Bayesian neural networksPlay Pac-Man using deep reinforcement learningDeploy scalable TensorFlow-based machine learning systemsGenerate your own book script using RNNsWho this book is for TensorFlow Machine Learning Projects is for you if you are a data analyst, data scientist, machine learning professional, or deep learning enthusiast with basic knowledge of TensorFlow. This book is also for you if you want to build end-to-end projects in the machine learning domain using supervised, unsupervised, and reinforcement learning techniques
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.
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.
Quantifying Structure In Random Forests
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Author : Hannah Sutton
language : en
Publisher:
Release Date : 2022
Quantifying Structure In Random Forests written by Hannah Sutton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Random forests are often regarded as black-box machine learning models. They are sufficiently complex that they are not easily interpretable. This fact has inspired a variety of research into improving the interpretability of random forests, which is the focus of this thesis; specifically, we wish to capture dissimilarities between random forest trees using several comparison functions on the decision trees that comprise the random forest, allowing the structure of the random forest to be quantified. These include a phylogenetic metric designed for transmission trees, as well as others we developed that involve the count and location of variables in each tree, as well as the depths of the trees. This allows us to visualise an underlying grouping of the trees using a heatmap and hierarchical clustering, and analyze the predictive accuracy of the decision tree clusters. Finally we propose a method for generating random decision trees, which we then use to generate synthetic data using a small set of trees. We use the random forest trained on this data to determine which comparison functions are statistically significant and contribute to the overall clustering. Additionally, we investigate whether or not the random forest is capable of recovering the original trees that the data was created from.
Analyzing Random Forests
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Author : Choongsoon Bae
language : en
Publisher:
Release Date : 2008
Analyzing Random Forests written by Choongsoon Bae and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Decision Forests For Computer Vision And Medical Image Analysis
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Author : Antonio Criminisi
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-01-30
Decision Forests For Computer Vision And Medical Image Analysis written by Antonio Criminisi 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 2013-01-30 with Computers categories.
This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. Topics and features: with a foreword by Prof. Y. Amit and Prof. D. Geman, recounting their participation in the development of decision forests; introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks; investigates both the theoretical foundations and the practical implementation of decision forests; discusses the use of decision forests for such tasks as classification, regression, density estimation, manifold learning, active learning and semi-supervised classification; includes exercises and experiments throughout the text, with solutions, slides, demo videos and other supplementary material provided at an associated website; provides a free, user-friendly software library, enabling the reader to experiment with forests in a hands-on manner.