Cars — Performance Report

2026-05-11 05:09

LightGBM · Colombia

Model Metrics

CV scores per fold

CV Scores per Fold
	fold_1	fold_2	fold_3	fold_4	fold_5
mse	158,859,537,106,690	193,426,599,303,793	175,320,438,660,755	152,380,457,106,725	143,889,480,937,557
rmse	12,603,949	13,907,789	13,240,862	12,344,248	11,995,394
mae	6,708,505	7,157,331	6,953,781	6,781,558	6,645,184
mape	0.1293	0.0848	0.3962	0.0841	0.3799
median_ae	3,722,886	3,839,066	3,746,337	3,732,294	3,660,400
r2	0.9688	0.9644	0.9663	0.9722	0.9724
explained_variance	0.9688	0.9644	0.9663	0.9722	0.9724

CV summary statistics

CV Summary Statistics
	count	mean	std	min	25%	50%	75%	max
mse	5.0000	164,775,302,623,104	19,729,056,744,039	143,889,480,937,557	152,380,457,106,725	158,859,537,106,690	175,320,438,660,755	193,426,599,303,793
rmse	5.0000	12,818,449	760,491.8	11,995,394	12,344,248	12,603,949	13,240,862	13,907,789
mae	5.0000	6,849,272	207,288.9	6,645,184	6,708,505	6,781,558	6,953,781	7,157,331
mape	5.0000	0.2149	0.1593	0.0841	0.0848	0.1293	0.3799	0.3962
median_ae	5.0000	3,740,197	64,308.6	3,660,400	3,722,886	3,732,294	3,746,337	3,839,066
r2	5.0000	0.9688	3.60e-03	0.9644	0.9663	0.9688	0.9722	0.9724
explained_variance	5.0000	0.9688	3.60e-03	0.9644	0.9663	0.9688	0.9722	0.9724

Test-set metrics

Test Set Metrics
Metric	Test Score
mse	170,099,519,479,083
rmse	13,042,221
mae	6,632,791
mape	0.0829
median_ae	3,523,207
r2	0.9668
explained_variance	0.9668

Regression Quality

Actual vs Predicted

Error magnitude

Residuals

Residuals vs Predicted

Residual distribution

Distributions

Actual vs Predicted distribution

Cumulative Error

Cumulative absolute error

Feature Importance

Feature importance