Apply the predicted tree on either new test data or training data.

Select features with p-value (computed from decision tree) < `p_thres` or all features if `show_all_feats == TRUE`.

prediction_df(fit, task, clust_samps, clust_target)

Arguments

fit

constparty object of the decision tree.

task

Character string indicating the type of problem, either 'classification' (categorical outcome) or 'regression' (continuous outcome).

clust_samps

Logical. If TRUE, hierarchical clustering would be performed among samples within each leaf node.

clust_target

Logical. If TRUE, target/label is included in hierarchical clustering of samples within each leaf node and might yield a more interpretable heatmap.

Value

A dataframe of prediction values with scaled columns and clustered samples.