Train multiple Decision Trees on different bootstrap samples of the data and average their predictions (bagging)

Train one very deep Decision Tree and increase min_samples_leaf to 1

Increase the number of features used at each split to include all available features

Train one Decision Tree and use it to generate synthetic training data for a second tree