Machine learning Path: The python support vector machine regression SVR predicts rates in Boston area

Python3 Learning using the API

Prediction of two kernel function models for support vector machines

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

 fromSklearn.datasetsImportLoad_boston fromSklearn.cross_validationImportTrain_test_split fromSklearn.preprocessingImportStandardscaler fromSklearn.svmImportSVR fromSklearn.metricsImportR2_score, Mean_squared_error, Mean_absolute_errorImportNumPy as NP#1 Preparing Data#Read the Boston area rate informationBoston =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#print ("Average price:", Np.mean (Boston.target)) # 22.532806324110677x=Boston.datay=Boston.target#2 split training data and test data#random sample 25% as test 75% as trainingX_train, X_test, y_train, y_test = Train_test_split (x, Y, test_size=0.25, random_state=33)#3 Standardized processing of training data and test dataSs_x =Standardscaler () X_train=ss_x.fit_transform (x_train) x_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))#4.1 Support vector machine model for learning and prediction#linear kernel function configuration support vector machineLinear_svr = SVR (kernel="Linear")#TrainingLinear_svr.fit (X_train, Y_train)#forecast Save Forecast resultsLinear_svr_y_predict =linear_svr.predict (x_test)#polynomial kernel function configuration support vector machinePoly_svr = SVR (kernel="Poly")#TrainingPoly_svr.fit (X_train, Y_train)#forecast Save Forecast resultsPoly_svr_y_predict =linear_svr.predict (x_test)#5 Model Evaluation#Evaluation of linear kernel function modelPrint("The default evaluation values for linear kernel function support vector machines are:", Linear_svr.score (X_test, y_test))Print("the r_squared value of the linear kernel function support vector machine is:", R2_score (Y_test, linear_svr_y_predict))Print("the mean square error of the linear kernel function support vector machine is:", Mean_squared_error (Ss_y.inverse_transform (y_test), SS_Y.INVERSE_TRANSFO RM (Linear_svr_y_predict )))Print("the mean absolute error of the linear kernel function support vector machine is:", Mean_absolute_error (Ss_y.inverse_transform (y_test), Ss_y.inverse_tra Nsform (linear_svr_y_predict)))#evaluation of a polynomial kernel function modelPrint("The default evaluation value for a polynomial kernel function is:", Poly_svr.score (X_test, y_test))Print("The r_squared value for the polynomial kernel function is:", R2_score (Y_test, poly_svr_y_predict))Print("The mean square error of the polynomial kernel function is:", Mean_squared_error (Ss_y.inverse_transform (y_test), Ss_y.inverse_transform (poly_svr_y_predict)))Print("The average absolute error for a polynomial kernel function is:", Mean_absolute_error (Ss_y.inverse_transform (y_test), ss_y.inverse_transf ORM (Poly_svr_y_predict )))" "the default evaluation value for the linear kernel function support vector machine is: 0.651717097429608 the r_squared value of the linear kernel function support vector machine is: 0.651717097429608 the mean square error of the linear kernel function support vector machine is: 27.0063071393243 the mean absolute error of the linear kernel function support vector machine is: 3.426672916872753 The default evaluation value for the polynomial kernel function is: 0.40445405800289286 The r_squared value of the polynomial kernel function is: 0.651717097429608 the mean square error of the polynomial kernel function is: 27.0063071393243 the mean absolute error of the polynomial kernel function is: 3.426672916872753" "

Machine learning Path: The python support vector machine regression SVR predicts rates in Boston area