均方根误差(Root Mean Square Error, RMSE):即均方误差开根号,方均根偏移代表预测的值和观察到的值之差的样本标准差 from sklearn.metrics import mean_squared_error np.sqrt(mean_squared_error(y_test,y_pre))#y_test为实际值,y_pre为预测值 1. 2. 51.563856309750065 1. 5.平均绝对误差MAE(Mean Absol...
from sklearn.metrics import mean_squared_errornp.sqrt(mean_squared_error(y_test,y_pre))#y_test为实际值,y_pre为预测值 51.563856309750065 5.平均绝对误差MAE(Mean Absolute Error) 平均绝对误差(Mean Absolute Error, MAE):是绝对误差的平均值,可以更好地反映预测值误差的实际情况 from sklearn.metrics im...
from sklearn.metrics import mean_squared_error #均方误差 from sklearn.metrics import mean_absolute_error #平方绝对误差 from sklearn.metrics import r2_score#R square #调用 MSE:mean_squared_error(y_test,y_predict) RMSE:np.sqrt(mean_squared_error(y_test,y_predict)) MAE:mean_absolute_error(y...
Adjusted R-squared is a reliable measure of goodness of fit for multiple regression problems. Discover the math behind it and how it differs from R-squared.
metrics.mean_squared_error, metrics.mean_absolute_error, ] specifics = ModelBuilder.metrics_from_list( ["sklearn.metrics.adjusted_mutual_info_score","sklearn.metrics.r2_score"] )assertspecifics == [metrics.adjusted_mutual_info_score, metrics.r2_score] ...
metrics.mean_squared_error, metrics.mean_absolute_error, ] specifics = ModelBuilder.metrics_from_list( ["sklearn.metrics.adjusted_mutual_info_score","sklearn.metrics.r2_score"] )assertspecifics == [metrics.adjusted_mutual_info_score, metrics.r2_score] ...
from sklearn.metrics import mean_squared_error, r2_score # 机器学习 from sklearn.preprocessing import StandardScaler from sklearn.linear_model import Lasso, LassoCV from sklearn.model_selection import train_test_split, cross_val_score from sklearn.ensemble import RandomForestRegressor ...