Book Image

Ensemble Machine Learning Cookbook

By : Dipayan Sarkar, Vijayalakshmi Natarajan
Book Image

Ensemble Machine Learning Cookbook

By: Dipayan Sarkar, Vijayalakshmi Natarajan

Overview of this book

Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking. The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis. By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes.

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Table of Contents (14 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Sections
Get in touch
Free Chapter
1
Get Closer to Your Data
Get Closer to Your Data
Introduction
Data manipulation with Python
Analyzing, visualizing, and treating missing values
Exploratory data analysis
2
Getting Started with Ensemble Machine Learning
Getting Started with Ensemble Machine Learning
Introduction to ensemble machine learning
Max-voting
Averaging
Weighted averaging
3
Resampling Methods
Resampling Methods
Introduction to sampling
k-fold and leave-one-out cross-validation
Bootstrapping
4
Statistical and Machine Learning Algorithms
Statistical and Machine Learning Algorithms
Technical requirements
Multiple linear regression
Logistic regression
Naive Bayes
Decision trees
Support vector machines
5
Bag the Models with Bagging
Bag the Models with Bagging
Introduction
Bootstrap aggregation
Ensemble meta-estimators
Bagging regressors
6
When in Doubt, Use Random Forests
When in Doubt, Use Random Forests
Introduction to random forests
Implementing a random forest for predicting credit card defaults using scikit-learn
Implementing random forest for predicting credit card defaults using H2O
7
Boosting Model Performance with Boosting
Boosting Model Performance with Boosting
Introduction to boosting
Implementing AdaBoost for disease risk prediction using scikit-learn
Implementing a gradient boosting machine for disease risk prediction using scikit-learn
Implementing the extreme gradient boosting method for glass identification using XGBoost with scikit-learn
8
Blend It with Stacking
Blend It with Stacking
Technical requirements
Understanding stacked generalization
Implementing stacked generalization by combining predictions
Implementing stacked generalization for campaign outcome prediction using H2O
9
Homogeneous Ensembles Using Keras
Homogeneous Ensembles Using Keras
Introduction
An ensemble of homogeneous models for energy prediction
An ensemble of homogeneous models for handwritten digit classification
10
Heterogeneous Ensemble Classifiers Using H2O
Heterogeneous Ensemble Classifiers Using H2O
Introduction
Predicting credit card defaulters using heterogeneous ensemble classifiers
11
Heterogeneous Ensemble for Text Classification Using NLP
Heterogeneous Ensemble for Text Classification Using NLP
Introduction
Spam filtering using an ensemble of heterogeneous algorithms
Sentiment analysis of movie reviews using an ensemble model
12
Homogenous Ensemble for Multiclass Classification Using Keras
Homogenous Ensemble for Multiclass Classification Using Keras
Introduction
An ensemble of homogeneous models to classify fashion products
13
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Bootstrapping

Bootstrapping is based on the jackknife method, which was proposed by Quenouille in 1949, and then refined by Tukey in 1958. The jackknife method is used for testing hypotheses and estimating confidence intervals. It's obtained by calculating the estimate after leaving out each observation and then computing the average of these calculations. With a sample of size N, the jackknife estimate can be found by aggregating the estimates of every N-1 sized sub-sample. It's similar to bootstrap samples, but while the bootstrap method is sampling with replacement, the jackknife method samples the data without replacement.

Bootstrapping is a powerful, non-parametric resampling technique that's used to assess the uncertainty in the estimator. In bootstrapping, a large number of samples with the same size are drawn repeatedly from an original sample. This allows...

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