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Feature Importance and Model Explainability

Explore how logistic regression coefficients reveal feature importance in predicting Titanic survival, and learn to interpret model explainability methods such as permutation importance, LIME, and SHAP values to understand the impact of features on predictions.

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Let's look at the linear models (linear regression, logistic regression, and their regularization extensions). We find a set of coefficients (β\beta values) for features in the training dataset, and then the weighted sum (coefficient_1×\timesfeature_1+⋯+ \cdots ...