skilearnAlgorithn.py 文件源码

def get_model_list():
    model_list, name_list = [], []

    # model_list.append(linear_model.LinearRegression())
    # name_list.append('LR')

    # model_list.append(gaussian_process.GaussianProcessRegressor(alpha=1e-10))
    # name_list.append('GaussianProcess')

    # model_list.append(KNeighborsRegressor(weights = 'uniform',n_neighbors=28))
    # name_list.append('KNN_unif')
    #
    # model_list.append(KNeighborsRegressor(weights = 'distance',n_neighbors=28))
    # name_list.append('KNN_dist')
    #
    # model_list.append(SVR(kernel = 'poly', C = 1, gamma = 'auto', coef0 = 0, degree = 2))
    # name_list.append('SVR_poly')
    # #
    model_list.append(SVR(kernel = 'rbf', C = 0.3, gamma = 'auto'))
    name_list.append('SVR_rbf')
    # #
    # model_list.append(DecisionTreeRegressor())
    # name_list.append('DT')
    #
    # model_list.append(RandomForestRegressor(n_estimators=150, max_depth=None,min_samples_split=2, random_state=0))
    # name_list.append('RF')
    #
    # model_list.append(ExtraTreesRegressor(n_estimators=150, max_depth=None, max_features='auto', min_samples_split=2, random_state=0))
    # name_list.append('ET')

    return model_list,name_list

#MAPE