from fbprophet.diagnostics import performance_metrics
df_p = performance_metrics(df_cv)
df_p.head()
|
horizon |
mse |
rmse |
mae |
mape |
coverage |
3297 |
37 days |
0.481970 |
0.694241 |
0.502930 |
0.058371 |
0.673367 |
35 |
37 days |
0.480991 |
0.693535 |
0.502007 |
0.058262 |
0.675879 |
2207 |
37 days |
0.480936 |
0.693496 |
0.501928 |
0.058257 |
0.675879 |
2934 |
37 days |
0.481455 |
0.693870 |
0.502999 |
0.058393 |
0.675879 |
393 |
37 days |
0.483990 |
0.695694 |
0.503418 |
0.058494 |
0.675879 |
mape平均绝对百分误差
定义
def evalmape(preds, dtrain):
gaps = dtrain.get_label()
err = abs(gaps-preds)/gaps
err[(gaps==0)] = 0
err = np.mean(err)*100
return 'error',err