Ensemble Machine Learning

An effective guide to using ensemble techniques to enhance machine learning models
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Ensemble Machine Learning

Ankit Dixit

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An effective guide to using ensemble techniques to enhance machine learning models

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Book Details

ISBN 139781788297752
Paperback438 pages

Book Description

Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior prediction power. This book will show you how you can use many weak algorithms to make a strong predictive model. This book contains Python code for different machine learning algorithms so that you can easily understand and implement it in your own systems.

This book covers different machine learning algorithms that are widely used in the practical world to make predictions and classifications. It addresses different aspects of a prediction framework, such as data pre-processing, model training, validation of the model, and more. You will gain knowledge of different machine learning aspects such as bagging (decision trees and random forests), Boosting (Ada-boost) and stacking (a combination of bagging and boosting algorithms).

Then you’ll learn how to implement them by building ensemble models using TensorFlow and Python libraries such as scikit-learn and NumPy. As machine learning touches almost every field of the digital world, you’ll see how these algorithms can be used in different applications such as computer vision, speech recognition, making recommendations, grouping and document classification, fitting regression on data, and more.

By the end of this book, you’ll understand how to combine machine learning algorithms to work behind the scenes and reduce challenges and common problems.

Table of Contents

Chapter 1: Introduction to Ensemble Learning
What is ensemble machine learning?
The purpose of ensemble machine learning
How to create an ensemble system
Quantification of performance
Bias and variance errors
 Methods to create ensemble systems
Summary
Chapter 2: Decision Trees
How do decision trees work?
ID3 algorithm for decision tree building
Case study – car evaluation problem
Summary
Chapter 3: Random Forest
Classification and regression trees
Case study – breast cancer type prediction
Decision tree bagging
Summary
Chapter 4: Random Subspace and KNN Bagging
Subspace bagging
Case study – subspace bagging
KNN classification 
KNN for spam filtering
KNN bagging with random subspaces
Summary
Chapter 5: AdaBoost Classifier
Boosting
AdaBoost in a nutshell
Application of the AdaBoost classifier in face detection
Summary
Chapter 6: Gradient Boosting Machines
Gradient Boosting Machines
Regression tree as a classifier
GBM implementation
Improvements to basic gradient boosting
Summary
Chapter 7: XGBoost – eXtreme Gradient Boosting
XGBoost – supervised learning
XGBoost features
Why use XGBoost?
How to install
XGBoost in action
XGBoost parameters
Summary
Chapter 8: Stacked Generalization
Stacked generalization
Submodel training
Stacked generalization implementation
Practical application – Sonar dataset (Mine and Rock prediction)
Summary
Chapter 9: Stacked Generalization – Part 2
Feature selection
Feature selection for machine learning
Understanding the SVM
Stacking of nonlinear algorithms
Summary
Chapter 10: Modern Day Machine Learning
Artificial Neural Networks (feed-forward)
Deep learning
Recurrent Neural Networks
Long Short-Term Memory networks
Summary
Chapter 11: Troubleshooting
Full code of the implemented algorithm ID3
Code of the CART algorithm
Code for random forest 
Code for KNN and subspace bagging
Code of the AdaBoost classifier
Code of GBMs
Full code of implementation
Full code of LSTM implementation

What You Will Learn

  • Understand why bagging improves classification and regression performance
  • Get to grips with implementing AdaBoost and different variants of this algorithm
  • See the bootstrap method and its application to bagging
  • Perform regression on Boston housing data using scikit-learn and NumPy
  • Know how to use Random forest for IRIS data classification
  • Get to grips with the classification of sonar dataset using KNN, Perceptron, and Logistic Regression
  • Discover how to improve prediction accuracy by fine-tuning the model parameters
  • Master the analysis of a trained predictive model for over-fitting/under-fitting cases

Authors

Table of Contents

Chapter 1: Introduction to Ensemble Learning
What is ensemble machine learning?
The purpose of ensemble machine learning
How to create an ensemble system
Quantification of performance
Bias and variance errors
 Methods to create ensemble systems
Summary
Chapter 2: Decision Trees
How do decision trees work?
ID3 algorithm for decision tree building
Case study – car evaluation problem
Summary
Chapter 3: Random Forest
Classification and regression trees
Case study – breast cancer type prediction
Decision tree bagging
Summary
Chapter 4: Random Subspace and KNN Bagging
Subspace bagging
Case study – subspace bagging
KNN classification 
KNN for spam filtering
KNN bagging with random subspaces
Summary
Chapter 5: AdaBoost Classifier
Boosting
AdaBoost in a nutshell
Application of the AdaBoost classifier in face detection
Summary
Chapter 6: Gradient Boosting Machines
Gradient Boosting Machines
Regression tree as a classifier
GBM implementation
Improvements to basic gradient boosting
Summary
Chapter 7: XGBoost – eXtreme Gradient Boosting
XGBoost – supervised learning
XGBoost features
Why use XGBoost?
How to install
XGBoost in action
XGBoost parameters
Summary
Chapter 8: Stacked Generalization
Stacked generalization
Submodel training
Stacked generalization implementation
Practical application – Sonar dataset (Mine and Rock prediction)
Summary
Chapter 9: Stacked Generalization – Part 2
Feature selection
Feature selection for machine learning
Understanding the SVM
Stacking of nonlinear algorithms
Summary
Chapter 10: Modern Day Machine Learning
Artificial Neural Networks (feed-forward)
Deep learning
Recurrent Neural Networks
Long Short-Term Memory networks
Summary
Chapter 11: Troubleshooting
Full code of the implemented algorithm ID3
Code of the CART algorithm
Code for random forest 
Code for KNN and subspace bagging
Code of the AdaBoost classifier
Code of GBMs
Full code of implementation
Full code of LSTM implementation

Book Details

ISBN 139781788297752
Paperback438 pages
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From 1 reviews

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