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