felse {false_tree __doc__})"fun num_leaves true_tree num_leaves false_tree num_leaves return fun learndt py -(continued def leaf_value(selfegsdomain)return self leaf_prediction((self target(efor in egs)domain def select_split(selfconditionsdata_subset)"""finds best feature to split on conditions is non-empty list of features returns featurepartition where feature is an input feature with the smallest error as judged by split_to_optimize or feature==none if there are no splits that improve the error partition is pair (false_examplestrue_examplesif feature is not none ""best_feat none best feature best_error float("inf"infinity more than any error best_error self sum_losses(data_subsetself gamma self display( ,no split has error=",best_error,"with",len(conditions),"conditions"best_partition none for feat in conditionsfalse_examplestrue_examples partition(data_subset,featif min(len(false_examples),len(true_examples))>=self min_child_weighterr (self sum_losses(false_examplesself sum_losses(true_examples)self display( ,split on",feat __doc__,"has error=",err"splits into",len(true_examples),":",len(false_examples),"gamma=",self gammaif err best_errorbest_feat feat best_error=err best_partition false_examplestrue_examples self display( ,"best split is on",best_feat __doc__"with err=",best_errorreturn best_featbest_partition def sum_losses(selfdata_subset)"""returns sum of losses for dataset (with no more splitsthere single prediction for all leaves using leaf_prediction it is evaluated using split_to_optimize ""prediction self leaf_value(data_subsetself target frangeerror sum(self split_to_optimize(predictionself target( )for in data_subsetversion june