In this chapter, we will cover the following recipes:
- Introduction to random forests
- Implementing a random forest for predicting credit card defaults using scikit-learn
- Implementing a random forest for predicting credit card defaults using H2O
When in Doubt, Use Random Forests
by Vijayalakshmi Natarajan, Dipayan Sarkar
Ensemble Machine Learning Cookbook
- Title Page
- Copyright and Credits
- About Packt
- Foreword
- Contributors
- Preface
- Get Closer to Your Data
- Getting Started with Ensemble Machine Learning
- Resampling Methods
- Statistical and Machine Learning Algorithms
- Bag the Models with Bagging
- When in Doubt, Use Random Forests
- 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
- Homogeneous Ensembles Using Keras
- Heterogeneous Ensemble Classifiers Using H2O
- Heterogeneous Ensemble for Text Classification Using NLP
- Homogenous Ensemble for Multiclass Classification Using Keras
- Other Books You May Enjoy
When in Doubt, Use Random Forests
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