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Ensemble Machine Learning Cookbook

You're reading from  Ensemble Machine Learning Cookbook

Product type Book
Published in Jan 2019
Publisher Packt
ISBN-13 9781789136609
Pages 336 pages
Edition 1st Edition
Languages
Authors (2):
Dipayan Sarkar Dipayan Sarkar
Profile icon Dipayan Sarkar
Vijayalakshmi Natarajan Vijayalakshmi Natarajan
Profile icon Vijayalakshmi Natarajan
View More author details
Toc

Table of Contents (14) Chapters close

Preface 1. Get Closer to Your Data 2. Getting Started with Ensemble Machine Learning 3. Resampling Methods 4. Statistical and Machine Learning Algorithms 5. Bag the Models with Bagging 6. When in Doubt, Use Random Forests 7. Boosting Model Performance with Boosting 8. Blend It with Stacking 9. Homogeneous Ensembles Using Keras 10. Heterogeneous Ensemble Classifiers Using H2O 11. Heterogeneous Ensemble for Text Classification Using NLP 12. Homogenous Ensemble for Multiclass Classification Using Keras 13. Other Books You May Enjoy

Introduction to sampling

Sampling techniques can be broadly classified into non-probability sampling techniques and probability sampling techniques. Non-probability sampling techniques are based on the judgement of the user, whereas in probability sampling, the observations are selected by chance.

Probability sampling most often includes simple random sampling (SRS), stratified sampling, and systematic sampling:

  • SRS: In SRS, each observation in the population has an equal probability of being chosen for the sample.
  • Stratified sampling: In stratified sampling, the population data is divided into separate groups, called strata. A probability sample is then drawn from each group.
  • Systematic sampling: In this method, a sample is drawn from the population by choosing observations at regular intervals.
If the sample is too small or too large, it may lead to incorrect findings. For...
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