learnboosting py -(continued testing from learndt import dt_learner from learnproblem import data_setdata_from_file def sp_dt_learner(split_to_optimize=evaluate squared_lossleaf_prediction=predict mean,**nargs)"""creates learner with different default arguments replaced by **nargs ""def new_learner(dataset)return dt_learner(dataset,split_to_optimize=split_to_optimizeleaf_prediction=leaf_prediction**nargsreturn new_learner #data data_from_file('data/car csv'target_index=- regression data data_from_file('data/student/student-mat-nq csv'separator=';',has_header=true,target_index=- ,seed= ,include_only=list(range( ))+[ ]# #data data_from_file('data/spect csv'target_index= seed= # #data data_from_file('data/mail_reading csv'target_index=- #data data_from_file('data/holiday csv'num_train= target_index=- #learner boosting_learner(datasp_dt_learner(split_to_optimize=evaluate squared_lossleaf_prediction=predict meanmin_child_weight= )#learner boosting_learner(datasp_dt_learner( )#learner boosting_learner(datasp_dt_learner( )#predictor =learner learn( #for in learner offsetsprint( __doc__import matplotlib pyplot as plt def plot_boosting_trees(datasteps= mcws=[ , , , ]gammas[ , , , ])to reduce clutter uncomment one of following two lines #mcws=[ #gammas=[ learners [(mcwgammaboosting_learner(datasp_dt_learner(min_child_weight=mcwgamma=gamma))for gamma in gammas for mcw in mcws plt ion(plt xscale('linear'change between log and linear scale plt xlabel("number of trees"plt ylabel("mean squared loss"markers ( + for in [' ',' ',' ',' ',' ',' ',' 'for in ['-','--','',':']for (mcw,gamma,learnerin learnersdata display( , "min_child_weight={mcw}gamma={gamma}"learner learn(stepsversion june