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BL
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| Record Number
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856557
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| Main Entry
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Tattar, Prabhanjan Narayanachar.
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| Title & Author
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Hands-On Ensemble Learning with R : : a Beginner's Guide to Combining the Power of Machine Learning Algorithms Using Ensemble Techniques.
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| Publication Statement
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Birmingham :: Packt Publishing Ltd,, 2018.
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| Page. NO
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1 online resource (376 pages)
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| ISBN
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1788624149
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: 1788629175
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: 9781788624145
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: 9781788629171
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| Notes
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Regression models.
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| Contents
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Cover; Copyright; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Ensemble Techniques; Datasets; Hypothyroid; Waveform; German Credit; Iris; Pima Indians Diabetes; US Crime; Overseas visitors; Primary Biliary Cirrhosis; Multishapes; Board Stiffness; Statistical/machine learning models; Logistic regression model; Logistic regression for hypothyroid classification; Neural networks; Neural network for hypothyroid classification; Naïve Bayes classifier; Naïve Bayes for hypothyroid classification; Decision tree; Decision tree for hypothyroid classification.
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Bootstrapping survival models*Bootstrapping time series models*; Summary; Chapter 3: Bagging; Technical requirements; Classification trees and pruning; Bagging; k-NN classifier; Analyzing waveform data; k-NN bagging; Summary; Chapter 4: Random Forests; Technical requirements; Random Forests; Variable importance; Proximity plots; Random Forest nuances; Comparisons with bagging; Missing data imputation; Clustering with Random Forest; Summary; Chapter 5: The Bare Bones Boosting Algorithms; Technical requirements; The general boosting algorithm; Adaptive boosting; Gradient boosting.
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Building it from scratchSquared-error loss function; Using the adabag and gbm packages; Variable importance; Comparing bagging, random forests, and boosting; Summary; Chapter 6: Boosting Refinements; Technical requirements; Why does boosting work?; The gbm package; Boosting for count data; Boosting for survival data; The xgboost package; The h2o package; Summary; Chapter 7: The General Ensemble Technique; Technical requirements; Why does ensembling work?; Ensembling by voting; Majority voting; Weighted voting; Ensembling by averaging; Simple averaging; Weight averaging; Stack ensembling.
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Support vector machinesSVM for hypothyroid classification; The right model dilemma!; An ensemble purview; Complementary statistical tests; Permutation test; Chi-square and McNemar test; ROC test; Summary; Chapter 2: Bootstrapping; Technical requirements; The jackknife technique; The jackknife method for mean and variance; Pseudovalues method for survival data; Bootstrap -- a statistical method; The standard error of correlation coefficient; The parametric bootstrap; Eigen values; Rule of thumb; The boot package; Bootstrap and testing hypotheses; Bootstrapping regression models.
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| Abstract
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Chapter 8: Ensemble Diagnostics; Technical requirements; What is ensemble diagnostics?; Ensemble diversity; Numeric prediction; Class prediction; Pairwise measure; Disagreement measure; Yule's or Q-statistic; Correlation coefficient measure; Cohen's statistic; Double-fault measure; Interrating agreement; Entropy measure; Kohavi-Wolpert measure; Disagreement measure for ensemble; Measurement of interrater agreement; Summary; Chapter 9: Ensembling Regression Models; Technical requirements; Pre-processing the housing data; Visualization and variable reduction; Variable clustering.
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This book introduces you to the concept of ensemble learning and demonstrates how different machine learning algorithms can be combined to build efficient machine learning models. Use R to implement the popular trilogy of ensemble techniques, i.e. bagging, random forest and boosting, to build faster and more accurate machine learning models.
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| Subject
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Computer algorithms.
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| Subject
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Machine learning.
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| Subject
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R.
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| Subject
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Computer algorithms.
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| Subject
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Machine learning.
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| Dewey Classification
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519.502855133
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| LC Classification
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QA276.45.R3.T388 2018
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