This article explores how multi-collinearity can damage causal inferences in marketing mix modeling and provides methods to address it, including Bayesian priors and random budget adjustments.
Cool question - and yes, you're right that you can use the summary command to inspect feature_importances for some of the models (e.g. RandomForestClassifier). Other models may not support the same type of summary however.
You should also check out the FieldSelector algorithm which is really useful for this problem. Under the hood, it uses ANOVA & F-Tests to estimate the linear dependency between variables. Although its univariate (not capturing any interactions between variables), it still can provide a good baseline from choosing a handful of features from hundreds.
You should also check out the FieldSelector algorithm which is really useful for this problem. Under the hood, it uses ANOVA & F-Tests to estimate the linear dependency between variables. Although its univariate (not capturing any interactions between variables), it still can provide a good baseline from choosing a handful of features from hundreds.
