Cars — Performance Report

2026-09-07 05:13

LightGBM · Colombia

Model Metrics

CV scores per fold

CV Scores per Fold
	fold_1	fold_2	fold_3	fold_4	fold_5
mse	198,140,841,309,035	184,872,195,382,111	192,416,299,622,249	175,191,070,335,372	195,708,045,434,472
rmse	14,076,251	13,596,772	13,871,420	13,235,976	13,989,569
mae	7,052,619	7,133,112	6,951,168	7,016,310	7,116,150
mape	0.5376	0.1696	0.1677	0.2687	0.5731
median_ae	3,801,293	3,792,581	3,703,429	3,756,228	3,651,843
r2	0.9681	0.9711	0.9680	0.9720	0.9674
explained_variance	0.9681	0.9711	0.9680	0.9720	0.9674

CV summary statistics

CV Summary Statistics
	count	mean	std	min	25%	50%	75%	max
mse	5.0000	189,265,690,416,648	9,321,967,455,364	175,191,070,335,372	184,872,195,382,111	192,416,299,622,249	195,708,045,434,472	198,140,841,309,035
rmse	5.0000	13,753,998	341,392.5	13,235,976	13,596,772	13,871,420	13,989,569	14,076,251
mae	5.0000	7,053,872	74,360.7	6,951,168	7,016,310	7,052,619	7,116,150	7,133,112
mape	5.0000	0.3434	0.1982	0.1677	0.1696	0.2687	0.5376	0.5731
median_ae	5.0000	3,741,075	63,024.2	3,651,843	3,703,429	3,756,228	3,792,581	3,801,293
r2	5.0000	0.9693	2.10e-03	0.9674	0.9680	0.9681	0.9711	0.9720
explained_variance	5.0000	0.9693	2.10e-03	0.9674	0.9680	0.9681	0.9711	0.9720

Test-set metrics

Test Set Metrics
Metric	Test Score
mse	161,146,691,032,865
rmse	12,694,357
mae	6,865,911
mape	0.2071
median_ae	3,754,191
r2	0.9733
explained_variance	0.9733

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