Baseline & Contributions: Explaining a Linear Score Feature by Feature
This lesson is part of the course
In this lesson
You predict the rent of eight flats with a toy linear model, compute the baseline (the average prediction), split every prediction into one contribution per feature and check that baseline plus contributions rebuilds the prediction.
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Lessons in this course
- Baseline & Contributions: Explaining a Linear Score Feature by Feature Locked
- Coalitions: All Eight Feature Subsets & the Model Output for Each Locked
- Shapley Values: Marginal Contributions over Every Order & the Weighted Formula Locked
- Interactions: How Shapley Splits an Effect Two Features Create Together Locked
- Permutation Importance: How Much Worse the Model Gets When a Column Is Shuffled Locked
- Partial Dependence & ICE Curves: One Feature Across the Whole Fleet Locked
- Counterfactuals: The Smallest Change That Flips a Decision Locked
- The Explanation Report: Global vs Local, Stability & Limitations Locked