Machine learning path: Python regression tree decisiontreeregressor forecast Boston Rates

Use of the Python3 learning API

Git:https://github.com/linyi0604/machinelearning

Code:

1  fromSklearn.datasetsImportLoad_boston2  fromSklearn.cross_validationImportTrain_test_split3  fromSklearn.preprocessingImportStandardscaler4  fromSklearn.treeImportDecisiontreeregressor5  fromSklearn.metricsImportR2_score, Mean_squared_error, Mean_absolute_error6 ImportNumPy as NP7 8 " "9 regression tree:Ten strictly speaking, the return tree is not a return . One The leaf node is a group of training data mean is not a continuous specific predicted value A      - solving the problem of characteristic nonlinearity - no requirement for standardization of features and uniform quantification the      - easy to be too complex to lose generalization ability - poor stability, subtle changes can lead to significant changes in tree structure -      + " " -  + #1 Preparing Data A #Read the Boston area rate information atBoston =Load_boston () - #View Data Description - #Print (Boston. DESCR) # A total of 506 Boston area rate information, each 13 numerical description and target rate - #view differences in Data - #print ("max rate:", Np.max (Boston.target)) # - #print ("Minimum rate:", Np.min (Boston.target)) # 5 in #print ("Average price:", Np.mean (Boston.target)) # 22.532806324110677 -  tox =Boston.data +y =Boston.target -  the #2 split training data and test data * #random sample 25% as test 75% as training $X_train, X_test, y_train, y_test = Train_test_split (x, Y, test_size=0.25, random_state=33)Panax Notoginseng  -  the #3 Standardized processing of training data and test data +Ss_x =Standardscaler () AX_train =ss_x.fit_transform (X_train) theX_test =ss_x.transform (x_test) +  -Ss_y =Standardscaler () $Y_train = Ss_y.fit_transform (Y_train.reshape (-1, 1)) $Y_test = Ss_y.transform (Y_test.reshape (-1, 1)) -  - #4 using regression trees for training and forecasting the #initializing K-Nearest neighbor regression model using average regression for prediction -DTR =decisiontreeregressor ()Wuyi #Training the Dtr.fit (X_train, Y_train) - #forecast Save Forecast results WuDtr_y_predict =dtr.predict (x_test) -  About #5 Model Evaluation $ Print("the default evaluation value for the regression tree is:", Dtr.score (X_test, y_test)) - Print("the r_squared values for the flat regression tree are:", R2_score (Y_test, dtr_y_predict)) - Print("the mean square error of the regression tree is:", Mean_squared_error (Ss_y.inverse_transform (y_test), - Ss_y.inverse_transform (dtr_y_predict))) A Print("the average absolute error of the regression tree is:", Mean_absolute_error (Ss_y.inverse_transform (y_test), + Ss_y.inverse_transform (dtr_y_predict))) the  - " " $ the default evaluation value for the regression tree is: 0.7066505912533438 the the r_squared value of the flat regression tree is: 0.7066505912533438 the the mean square error of the regression tree is: 22.746692913385836 the the average absolute error of the regression tree is: 3.08740157480315 the " "

Machine learning path: Python regression tree decisiontreeregressor forecast Boston Rates