A Gentle Introduction to Predictive Modelling

Introduction
1. Python Basics
- Introduction to python
- Working with DataFrames
2. Linear Models and Overfitting
3. Unbiased predictive performance estimates
- Training and Test sets
- Cross-validation
4. Fighting Overfitting - The Advent of Machine Learning
5. Hyperparameter Optimization and Nested Cross-Validation
- Cross-validation Revisited
- Nested Cross-validation
6. Generalizability, Validity, Fairness
7. Model Explanation
- Explainability of “Classical” Models
- Explainability vs. Black Box Models
8. Complex Models: from Ensembles to Deep Learning
9. Scientific Examples
References

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7. Model Explanation

7. Model Explanation¶

In this chapter:

Explainability of “Classical” Models’
Explainability vs. Black Box Models

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Fairness

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Explainability of “Classical” Models

By Tamas Spisak
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