We'll now get to an overview of the techniques, covering classification trees, random forests, and gradient boosting. This will set the stage for their practical use.
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Preparing and Understanding Data
- Linear Regression
- Logistic Regression
- Advanced Feature Selection in Linear Models
- K-Nearest Neighbors and Support Vector Machines
- Tree-Based Classification
- Neural Networks and Deep Learning
- Creating Ensembles and Multiclass Methods
- Cluster Analysis
- Principal Component Analysis
- Association Analysis
- Time Series and Causality
- Text Mining
- Creating a Package
- Other Books You May Enjoy
An overview of the techniques
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