拟合回归模型后如何打印 MAPE（平均绝对误差百分比）？

Question

我训练了一个 XGB 回归器，现在我想输出 MAPE 分数，但我不确定如何输出。这是我的代码：

from numpy import absolute
from pandas import read_csv
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedKFold
from xgboost import XGBRegressor

# define model
model = XGBRegressor()

# define model evaluation method
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)

# evaluate model
scores = cross_val_score(model, X, y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)

Answer 1

您可以使用以下任一选项：

from sklearn.metrics import mean_absolute_error
mape = mean_absolute_error(Y_actual, Y_Predicted)*100`

或者，

mape = np.mean(np.abs((Y_actual - Y_Predicted)/Y_actual))*100

拟合回归模型后如何打印 MAPE（平均绝对误差百分比）？

How to print MAPE (mean abs. % error) after fitting regression model?

python

machine-learning

xgboost