Use the TreeModel class from the pyspark.ml.tree module to obtain the feature importances and prediction details of a decision tree model.

Use the TreeExplainer class from the pyspark.ml.explainers module to explain the predictions of a tree-based model by showing the contribution of each feature to the prediction.

Use the summary attribute of a fitted TrainValidationSplitModel or CrossValidatorModel to obtain the feature importances and model statistics.

Use the coefficients attribute of a fitted LogisticRegression model to obtain the feature importances and model coefficients.