XGBoost is a boosting library with parallel, GPU, and distributed execution support. It has helped many machine learning engineers and data scientists to win Kaggle.com competitions. Furthermore, it provides an interface that resembles scikit-learn's interface. Thus, someone already familiar with the interface is able to quickly utilize the library. Additionally, it allows for very fine control over the ensemble's creation. It supports monotonic constraints (that is, the predicted value should only increase or decrease, relative to a specific feature), as well as feature interaction constraints (for example, if a decision tree creates a node that splits by age, it should not use sex as a splitting feature for all children of that specific node). Finally, it adds an additional regularization parameter, gamma, which further reduces the overfitting capabilities...
Hands-On Ensemble Learning with Python
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Hands-On Ensemble Learning with Python
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Overview of this book
Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model.
With its hands-on approach, you'll not only get up to speed with the basic theory but also the application of different ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. In addition to this, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models.
By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios.
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Current Title:
Hands-On Ensemble Learning with Python
Table of Contents (20 chapters)
Preface
Free Chapter
Section 1: Introduction and Required Software Tools
A Machine Learning Refresher
Getting Started with Ensemble Learning
Section 2: Non-Generative Methods
Voting
Stacking
Section 3: Generative Methods
Bagging
Boosting
Random Forests
Section 4: Clustering
Section 5: Real World Applications
Classifying Fraudulent Transactions
Predicting Bitcoin Prices
Evaluating Sentiment on Twitter
Recommending Movies with Keras
Clustering World Happiness
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