Tree Based Machine Learning Algorithms
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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.
Tree Based Methods For Statistical Learning In R
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Author : Brandon M. Greenwell
language : en
Publisher: CRC Press
Release Date : 2022-06-23
Tree Based Methods For Statistical Learning In R written by Brandon M. Greenwell and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-23 with Business & Economics categories.
Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level, which lie at the cutting edge of modern statistical and machine learning methodology. The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example, users will be exposed to writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software (like rpart and party/partykit), and more. The core chapters also end with a detailed section on relevant software in both R and other opensource alternatives (e.g., Python, Spark, and Julia), and example usage on real data sets. While the book mostly uses R, it is meant to be equally accessible and useful to non-R programmers. Consumers of this book will have gained a solid foundation (and appreciation) for tree-based methods and how they can be used to solve practical problems and challenges data scientists often face in applied work. Features: Thorough coverage, from the ground up, of tree-based methods (e.g., CART, conditional inference trees, bagging, boosting, and random forests). A companion website containing additional supplementary material and the code to reproduce every example and figure in the book. A companion R package, called treemisc, which contains several data sets and functions used throughout the book (e.g., there’s an implementation of gradient tree boosting with LAD loss that shows how to perform the line search step by updating the terminal node estimates of a fitted rpart tree). Interesting examples that are of practical use; for example, how to construct partial dependence plots from a fitted model in Spark MLlib (using only Spark operations), or post-processing tree ensembles via the LASSO to reduce the number of trees while maintaining, or even improving performance.
Extensions To Bayesian Tree Based Machine Learning Algorithms
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Author : Estevao Batista do Prado
language : en
Publisher:
Release Date : 2022
Extensions To Bayesian Tree Based Machine Learning Algorithms written by Estevao Batista do Prado and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Hamilton Institute Theses categories.
Tree Based Machine Learning For Data Classification
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Author : Sivanagireddy Kalli
language : en
Publisher:
Release Date : 2022
Tree Based Machine Learning For Data Classification written by Sivanagireddy Kalli 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.
Advancement in both information technology and human activities leads to the accelerated generation of high-speed continuous data. Consequently, data scientists and researchers have been facing many challenges in data mining. In light of this, AI has developed a powerful human-machine interaction approach called machine learning. Machine learning is a multidisciplinary domain that plays a key role in classification, regression, clustering, and predication that has led to tremendous research progress recently. However classification plays an important role in big data analysis, data mining, pattern reorganization, computer vision, language processing and many more. Several data classifiers have been proposed by researchers from various fields in many ways. However, decision tree classifiers are tree-based classifiers and the most prominent data classification algorithms in data mining and machine learning. This paper investigates and summarizes various machine learning algorithms, techniques, capabilities, and limitations. Further study various benchmark tree-based decision tree classifiers and identify the challenges in data mining, machine learning especially data stream mining. In addition, all of the decision tree algorithms were analyzed, illustrating by analyzing a sample dataset, the logic and identifying the most accurate classifier for data classification.
Tree Based Machine Learning Methods In Sas Viya
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Author : Sharad Saxena
language : en
Publisher:
Release Date : 2022-02-21
Tree Based Machine Learning Methods In Sas Viya written by Sharad Saxena and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-21 with Computers categories.
Discover how to build decision trees using SASViya! Tree-Based Machine Learning Methods in SASViya covers everything from using a single tree to more advanced bagging and boosting ensemble methods. The book includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees, forests, and gradient boosted trees. Each chapter introduces a new data concern and then walks you through tweaking the modeling approach, modifying the properties, and changing the hyperparameters, thus building an effective tree-based machine learning model. Along the way, you will gain experience making decision trees, forests, and gradient boosted trees that work for you. By the end of this book, you will know how to: build tree-structured models, including classification trees and regression trees. build tree-based ensemble models, including forest and gradient boosting. run isolation forest and Poisson and Tweedy gradient boosted regression tree models. implement open source in SAS and SAS in open source. use decision trees for exploratory data analysis, dimension reduction, and missing value imputation.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Artificial intelligence categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Machine Learning With Python Cookbook
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Author : Chris Albon
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-03-09
Machine Learning With Python Cookbook written by Chris Albon and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-09 with Computers categories.
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models
Data Mining With Decision Trees Theory And Applications 2nd Edition
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Author : Oded Z Maimon
language : en
Publisher: World Scientific
Release Date : 2014-09-03
Data Mining With Decision Trees Theory And Applications 2nd Edition written by Oded Z Maimon and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-03 with Computers categories.
Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.This book invites readers to explore the many benefits in data mining that decision trees offer:
Hands On Machine Learning With Scikit Learn And Scientific Python Toolkits
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Author : Tarek Amr
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-24
Hands On Machine Learning With Scikit Learn And Scientific Python Toolkits written by Tarek Amr 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 2020-07-24 with Mathematics categories.
Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems Key FeaturesDelve into machine learning with this comprehensive guide to scikit-learn and scientific PythonMaster the art of data-driven problem-solving with hands-on examplesFoster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithmsBook Description Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits. The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance, you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms. By the end of this machine learning book, you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production. What you will learnUnderstand when to use supervised, unsupervised, or reinforcement learning algorithmsFind out how to collect and prepare your data for machine learning tasksTackle imbalanced data and optimize your algorithm for a bias or variance tradeoffApply supervised and unsupervised algorithms to overcome various machine learning challengesEmploy best practices for tuning your algorithm’s hyper parametersDiscover how to use neural networks for classification and regressionBuild, evaluate, and deploy your machine learning solutions to productionWho this book is for This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.
Machine Learning And Data Science Blueprints For Finance
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Author : Hariom Tatsat
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-10-01
Machine Learning And Data Science Blueprints For Finance written by Hariom Tatsat and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-01 with Computers categories.
Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations