Interpreting Logistic Regression Coefficients and Feature Importance
Summary
The document asks whether feature importance can be obtained from logistic regression, including Lasso-regularized models. Its reply contrasts tree-based importance measures with coefficient-based models and notes that a platform with scikit-learn installed can be used to fit such models. The central practical distinction is that logistic regression does not produce the same built-in split-based importance measure as a tree; its fitted coefficients are the natural quantities to inspect.
The response is brief and does not explain how to interpret coefficient magnitude, account for feature scaling, or assess uncertainty and correlated predictors. For Lasso, regularization can shrink some coefficients to zero, but that alone does not establish a feature’s general predictive value. No model results or examples are supplied. The answer’s suggestion that tree models alone have feature importance is too narrow: coefficients can offer useful model-specific information, though comparisons require care and validation.
Key ideas
- Logistic regression represents feature effects through fitted coefficients rather than tree split importance.
- Lasso regularization can shrink coefficients, including some to zero.
- Coefficient magnitudes require care because feature scales and correlations affect interpretation.
- The response provides no example, validation results, or uncertainty analysis.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.