supervised machine learning it only splits if the best split increases the error by at least gamma this implies it does not split whenthere are no more input features there are fewer examples than min number examplesall the examples agree on the value of the targetor the best split makes all examples in the same partition if it splitsit selects the best split according to the evaluation criterion (assuming that is the only split it gets to do)and returns the condition to split on (in the variable splitand the corresponding partition of the examples learndt py -(continued def learn_tree(selfconditionsdata_subset)"""returns decision tree conditions is set of possible conditions data_subset is subset of the data used to build this (sub)tree where decision tree is function that takes an example and makes prediction on the target feature ""self display( , "learn_tree with {len(conditions)features and {len(data_subset)examples"splitpartn self select_split(conditionsdata_subsetif split is noneno splitreturn point prediction prediction self leaf_value(data_subsetself target frangeself display( , "leaf prediction for {len(data_subset)examples is {prediction}"def leaf_fun( )return prediction leaf_fun __doc__ str(predictionleaf_fun num_leaves return leaf_fun elsea split succeeded false_examplestrue_examples partn rem_features [fe for fe in conditions if fe !splitself display( ,"splitting on",split __doc__,"with examples split"len(true_examples),":",len(false_examples)true_tree self learn_tree(rem_features,true_examplesfalse_tree self learn_tree(rem_features,false_examplesdef fun( )if split( )return true_tree(eelsereturn false_tree( #fun lambda etrue_tree(eif split(eelse false_tree(efun __doc__ ( "(if {split __doc__then {true_tree __doc__}version june