使用了三种集成回归模型 git: https://github.com/linyi0604/MachineLearning 代码: 1fromsklearn.datasetsimportload_boston2fromsklearn.cross_validationimporttrain_test_split3fromsklearn.preprocessingimportStandardScaler4fromsklearn.ensembleimportRandomForestRegressor, ExtraTreesRegressor, GradientBoostingRegressor5from...
(Darwin, Sydney, CoffsHarbour, Perth, and Moree) through logistic regression, K-Nearest Neighbor (KNN), decision tree, random forest, Support Vector Machine (SVM) and Artificial Neural Network (ANN) using Synthetic Minority Oversampling TEchnique (SMOTE) and ExtraTreesRegressor, the pre-processing...
(Darwin, Sydney, CoffsHarbour, Perth, and Moree) through logistic regression, K-Nearest Neighbor (KNN), decision tree, random forest, Support Vector Machine (SVM) and Artificial Neural Network (ANN) using Synthetic Minority Oversampling TEchnique (SMOTE) and ExtraTreesRegressor, the pre-processing...
4.1. Random Forest Regressor The aim of the random forest regressor is to build a reliable and precise predictive model for tasks involving classification or regression. In a random forest, the ensemble is made up of many decision trees that have been generated and assembled. A random subset of...
ensemble import ExtraTreesRegressor # define dataset X, y = make_regression(n_samples=1000, n_features=20, n_informative=15, noise=0.1, random_state=3) # define the model model = ExtraTreesRegressor() # evaluate the model cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)...