Description
Main topics Covered in the book
- Data preprocessing
- Training/test splitting
- Cross-validation and resampling
- Feature selection
- Regression models
- Classification models
- Decision trees
- Random forests
- Boosting
- Neural networks
- Support vector machines
- Model interpretation and variable importance
- Handling class imbalance
- Model performance assessment
Mathematical level
Compared with other well-known texts:
| Book | Math Level | Practical Focus |
|---|---|---|
| An Introduction to Statistical Learning | Low–Moderate | Very High |
| Applied Predictive Modeling | Moderate | Extremely High |
| The Elements of Statistical Learning | High | High |
| Mathematical Statistics with Applications | High | Moderate |
Best use case
This book is particularly valuable if your goal is:
- Data Science
- Machine Learning Engineering
- Predictive Analytics
- Applied Statistics in industry
- Learning practical modeling workflows rather than proving theoretical results
Recommended study sequence
For a strong foundation in statistics and machine learning:
- Mathematical Statistics with Applications
- Applied Predictive Modeling
- An Introduction to Statistical Learning
- The Elements of Statistical Learning
This progression takes you from probability and inference, through practical predictive modeling, and then into modern statistical learning theory.
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