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.
In this post, I’ll discuss random forests, another popular approach for feature ranking.
Random forest feature importance
Random forests are among the most popular machine learning methods thanks to their relatively good accuracy, robustness and ease of use. They also provide two straightforward methods for feature selection: mean decrease impurity and mean decrease accuracy.
Random forest feature importance
Random forests are among the most popular machine learning methods thanks to their relatively good accuracy, robustness and ease of use. They also provide two straightforward methods for feature selection: mean decrease impurity and mean decrease accuracy.
