Ensemble Machine Learning Cookbook

By : Dipayan Sarkar, Vijayalakshmi Natarajan

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Sections

Get in touch

Free Chapter

Get Closer to Your Data

Introduction

Data manipulation with Python

Analyzing, visualizing, and treating missing values

Exploratory data analysis

Getting Started with Ensemble Machine Learning

Introduction to ensemble machine learning

Max-voting

Averaging

Weighted averaging

Resampling Methods

Introduction to sampling

k-fold and leave-one-out cross-validation

Bootstrapping

Statistical and Machine Learning Algorithms

Technical requirements

Multiple linear regression

Logistic regression

Naive Bayes

Decision trees

Support vector machines

Bag the Models with Bagging

Introduction

Bootstrap aggregation

Ensemble meta-estimators

Bagging regressors

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

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

Blend It with Stacking

Technical requirements

Understanding stacked generalization

Implementing stacked generalization by combining predictions

Implementing stacked generalization for campaign outcome prediction using H2O

Homogeneous Ensembles Using Keras

Introduction

An ensemble of homogeneous models for energy prediction

An ensemble of homogeneous models for handwritten digit classification

Heterogeneous Ensemble Classifiers Using H2O

Introduction

Predicting credit card defaulters using heterogeneous ensemble classifiers

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

Homogenous Ensemble for Multiclass Classification Using Keras

Introduction

An ensemble of homogeneous models to classify fashion products

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Implementing a random forest for predicting credit card defaults using scikit-learn

The scikit-learn library implements random forests by providing two estimators: RandomForestClassifier and RandomForestRegressor. They take various parameters, some of which are explained as follows:

n_estimators: This parameter is the number of trees the algorithm builds before taking a maximum vote or the average prediction. In general, the higher the number of trees the better the performance and the accuracy of the predictions, but it also costs more in terms of computation.
max_features: This parameter is the maximum number of features that the random forest is allowed to try in an individual tree.
min_sample_leaf: This parameter determines the minimum number of leaves that are required to split an internal node.
n_jobs: This hyperparameter tells the engine how many jobs to run in parallel...

Ensemble Machine Learning Cookbook

By : Dipayan Sarkar, Vijayalakshmi Natarajan

Ensemble Machine Learning Cookbook

By: Dipayan Sarkar, Vijayalakshmi Natarajan

Overview of this book

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