Machine learning Path: The python K-nearest neighbor regression predicts Boston rates

Python3 Learning Machine Learning API

Two k-Nearest neighbor regression models were used to predict the mean K nearest neighbor regression and distance weighted K-nearest neighbor regression.

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

Code:

1  fromSklearn.datasetsImportLoad_boston2  fromSklearn.cross_validationImportTrain_test_split3  fromSklearn.preprocessingImportStandardscaler4  fromSklearn.neighborsImportKneighborsregressor5  fromSklearn.metricsImportR2_score, Mean_squared_error, Mean_absolute_error6 ImportNumPy as NP7 8 #1 Preparing Data9 #Read the Boston area rate informationTenBoston =Load_boston () One #View Data Description A #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)) # the #print ("Minimum rate:", Np.min (Boston.target)) # 5 - #print ("Average price:", Np.mean (Boston.target)) # 22.532806324110677 -  -x =Boston.data +y =Boston.target -  + #2 split training data and test data A #random sample 25% as test 75% as training atX_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 data -Ss_x =Standardscaler () -X_train =ss_x.fit_transform (X_train) inX_test =ss_x.transform (x_test) -  toSs_y =Standardscaler () +Y_train = Ss_y.fit_transform (Y_train.reshape (-1, 1)) -Y_test = Ss_y.transform (Y_test.reshape (-1, 1)) the  * #study and prediction of 42 K-Nearest neighbor regression lines $ #initializing K-Nearest neighbor regression model using average regression for predictionPanax NotoginsengUNI_KNR = Kneighborsregressor (weights="Uniform") - #Training the Uni_knr.fit (X_train, Y_train) + #forecast Save Forecast results AUni_knr_y_predict =uni_knr.predict (x_test) the  + #Multi-initialized K-nearest neighbor regression model using distance weighted regression -DIS_KNR = Kneighborsregressor (weights="Distance") $ #Training $ Dis_knr.fit (X_train, Y_train) - #forecast Save Forecast results -Dis_knr_y_predict =dis_knr.predict (x_test) the  - #5 Model EvaluationWuyi #evaluation of the average K-nearest neighbor regression model the Print("the default evaluation value for the average K-nearest neighbor regression is:", Uni_knr.score (X_test, y_test)) - Print("the r_squared value of the average K-nearest neighbor regression is:", R2_score (Y_test, uni_knr_y_predict)) Wu Print("the mean square error of the average K nearest neighbor regression is:", Mean_squared_error (Ss_y.inverse_transform (y_test), - Ss_y.inverse_transform (uni_knr_y_predict))) About Print("the average absolute error of the average K-nearest neighbor regression is:", Mean_absolute_error (Ss_y.inverse_transform (y_test), $ Ss_y.inverse_transform (uni_knr_y_predict))) - #evaluation of distance weighted K-nearest neighbor regression model - Print("the default evaluation value for distance weighted K-nearest neighbor regression is:", Dis_knr.score (X_test, y_test)) - Print("the r_squared value of the distance weighted K-nearest neighbor regression is:", R2_score (Y_test, dis_knr_y_predict)) A Print("the mean square error of the distance weighted K nearest neighbor regression is:", Mean_squared_error (Ss_y.inverse_transform (y_test), + Ss_y.inverse_transform (dis_knr_y_predict))) the Print("the average absolute error of the distance weighted K-nearest neighbor regression is:", Mean_absolute_error (Ss_y.inverse_transform (y_test), - Ss_y.inverse_transform (dis_knr_y_predict))) $  the " " the the default evaluation value for the average K-nearest neighbor regression is: 0.6903454564606561 the the r_squared value of the average K-nearest neighbor regression is: 0.6903454564606561 the Mean square error of average K nearest neighbor regression is: 24.01101417322835 - the average absolute error of the average K-nearest neighbor regression is: 2.9680314960629928 in the default evaluation value for distance weighted K-nearest neighbor regression is: 0.7197589970156353 the the r_squared value of the distance weighted K-nearest neighbor regression is: 0.7197589970156353 the the mean square error of the distance weighted K nearest neighbor regression is: 21.730250160926044 About the average absolute error of the distance weighted K-nearest neighbor regression is: 2.8050568785108005 the " "

Machine learning Path: The python K-nearest neighbor regression predicts Boston rates