Ensemble Methods in Data Mining Improving Accuracy Through Combining Predictions 1st Edition by Giovanni Seni, John Elder, Robert Grossman ISBN 1608452840 9781608452842

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ISBN 10: 1608452840
ISBN 13: 9781608452842
Author: Giovanni Seni, John Elder, Robert Grossman

Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges — from investment timing to drug discovery, and fraud detection to recommendation systems — where predictive accuracy is more vital than model interpretability.

Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization — today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods — bagging, random forests, and boosting — to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity.

This book is aimed at novice and advanced analytic researchers and practitioners — especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques.

Ensemble Methods in Data Mining Improving Accuracy Through Combining Predictions 1st Table of contents:

Chapter 1: Introduction to Data Mining

• What is Data Mining?
• Overview of Data Mining Techniques
• Applications of Data Mining in Real-World Problems
• The Need for Combining Multiple Models

Chapter 2: Fundamentals of Ensemble Methods

• What are Ensemble Methods?
• Types of Ensemble Methods: Bagging, Boosting, Stacking, and Voting
• Basic Concepts: Bias-Variance Tradeoff, Model Diversity
• Theoretical Foundations of Ensemble Learning

Chapter 3: Bagging Methods (Bootstrap Aggregating)

• Concept and Mechanism of Bagging
• The Random Forest Algorithm
• Benefits and Drawbacks of Bagging
• Practical Applications and Case Studies

Chapter 4: Boosting Methods

• Introduction to Boosting and Adaptive Boosting (AdaBoost)
• Gradient Boosting Machines (GBM) and XGBoost
• Comparison of Boosting and Bagging
• Practical Implementation and Applications

Chapter 5: Stacking Methods

• What is Stacking?
• Combining Models Using Meta-Learners
• Theoretical Basis for Stacking
• Case Studies and Practical Use Cases

Chapter 6: Voting and Averaging Methods

• Simple Voting vs. Weighted Voting
• Majority Voting and Probabilistic Voting
• Combining Regression Models Using Averaging
• Applications in Classification and Regression Problems

Chapter 7: Model Selection and Evaluation

• Performance Metrics for Ensemble Methods
• Cross-Validation and Model Selection Strategies
• Handling Overfitting in Ensemble Models
• Comparison of Ensemble Methods with Single Learners

Chapter 8: Advanced Ensemble Methods

• Stacked Generalization
• Negative Correlation Based Methods
• Combining Neural Networks with Ensemble Methods
• Hybrid Ensemble Approaches

Chapter 9: Ensemble Methods for Imbalanced Data

• The Challenge of Imbalanced Data in Classification Problems
• Modifying Ensemble Techniques for Imbalanced Datasets
• Cost-sensitive Learning with Ensemble Methods
• Case Studies in Imbalanced Data Applications

Chapter 10: Practical Implementation and Tools

• Implementing Ensemble Methods in Popular Machine Learning Libraries (e.g., scikit-learn, XGBoost)
• Choosing the Right Ensemble Method for Different Problems
• Performance Optimization Techniques
• Code Examples and Best Practices

Chapter 11: Future Directions and Challenges

• The Role of Ensemble Methods in Deep Learning
• Innovations in Ensemble Methods for Large-Scale Data
• Combining Ensemble Methods with Transfer Learning
• Ethical Considerations and Transparency in Ensemble Learning

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