Cargando…
Hands-On Ensemble Learning with R : a Beginner's Guide to Combining the Power of Machine Learning Algorithms Using Ensemble Techniques.
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; Interra...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000Mi 4500 | ||
|---|---|---|---|
| 001 | EBOOKCENTRAL_on1048793117 | ||
| 003 | OCoLC | ||
| 005 | 20240329122006.0 | ||
| 006 | m o d | ||
| 007 | cr |n|---||||| | ||
| 008 | 180818s2018 enk o 000 0 eng d | ||
| 040 | |a EBLCP |b eng |e pn |c EBLCP |d MERUC |d CHVBK |d IDB |d OCLCO |d OCLCF |d OCLCQ |d UKAHL |d OCLCQ |d UX1 |d K6U |d NLW |d OCLCO |d OCLCQ |d OCLCO |d OCLCL | ||
| 019 | |a 1175631865 | ||
| 020 | |a 9781788629171 | ||
| 020 | |a 1788629175 | ||
| 020 | |a 9781788624145 | ||
| 020 | |a 1788624149 |q (Trade Paper) | ||
| 024 | 3 | |a 9781788624145 | |
| 029 | 1 | |a AU@ |b 000066230910 | |
| 029 | 1 | |a CHNEW |b 001024161 | |
| 029 | 1 | |a CHVBK |b 530304716 | |
| 035 | |a (OCoLC)1048793117 |z (OCoLC)1175631865 | ||
| 037 | |a B09012 |b 01201872 | ||
| 050 | 4 | |a QA276.45.R3 |b .T388 2018 | |
| 082 | 0 | 4 | |a 519.502855133 |2 23 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Tattar, Prabhanjan Narayanachar. | |
| 245 | 1 | 0 | |a Hands-On Ensemble Learning with R : |b a Beginner's Guide to Combining the Power of Machine Learning Algorithms Using Ensemble Techniques. |
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2018. | ||
| 300 | |a 1 online resource (376 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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. | ||
| 500 | |a Regression models. | ||
| 520 | |a 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. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a R. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Computer algorithms. | |
| 650 | 2 | |a Algorithms | |
| 650 | 2 | |a Machine Learning | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Algorithmes. | |
| 650 | 7 | |a algorithms. |2 aat | |
| 650 | 7 | |a Data capture & analysis. |2 bicssc | |
| 650 | 7 | |a Mathematical theory of computation. |2 bicssc | |
| 650 | 7 | |a Machine learning. |2 bicssc | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Computers |x Data Processing. |2 bisacsh | |
| 650 | 7 | |a Computers |x Machine Theory. |2 bisacsh | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Computer algorithms |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 758 | |i has work: |a Hands-on ensemble learning with R (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFVxkJQXtfhxX3W9cp8yFq |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Tattar, Prabhanjan Narayanachar. |t Hands-On Ensemble Learning with R : A Beginner's Guide to Combining the Power of Machine Learning Algorithms Using Ensemble Techniques. |d Birmingham : Packt Publishing Ltd, ©2018 |z 9781788624145 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5482822 |z Texto completo |
| 938 | |a Askews and Holts Library Services |b ASKH |n BDZ0037625901 | ||
| 938 | |a EBL - Ebook Library |b EBLB |n EBL5482822 | ||
| 994 | |a 92 |b IZTAP | ||