the output shows sudden improvement in score as depth increases from to while gradual improvement as we reach from the impact of number of leaf nodes is rather interesting the difference in scores between and leaf nodes is strikingly not much this is clear indicator that further finetuning is possible figure - depicts the visualization showcasing these results figure - mean test score and impact of tree depth and count of leaf nodes as mentionedthere is still scope of fine-tuning to further improve the results it is therefore decision which is use case and cost dependent cost can be in terms of efforttime and corresponding improvements achieved for nowwe'll proceed with the best model our gridsearchcv object has helped us identify testing once we have model trained with optimized hyperparameterswe can begin with our usual workflow to test performance on an unseen dataset we will utilize the same preprocessing as discussed while preparing test set for linear regression (you may refer to the "testingsection of linear regression and/or the jupyter notebook decision_tree_regression ipynbthe following snippet predicts the output values for the test dataset using the best estimator achieved during training phase in [ ]y_pred best_dtr_model predict(x_testresiduals y_test flatten(y_pred the final step is to view the -squared score on this dataset well-fit model should have comparable performance on this set as wellthe same is evident from the following snippet in [ ]print(" -squared::{}format( _score) -squared:: as is evident from the -squared valuethe performance is quite comparable to our training performance we can conclude by saying decision tree regressor was better at forecasting bike demand as compared to linear regression