AutoML Leaderboard

Best model	name	model_type	metric_type	metric_value	train_time
	1_Baseline	Baseline	rmse	22.3189	27.94
	2_DecisionTree	Decision Tree	rmse	12.8338	3494.55
	3_DecisionTree	Decision Tree	rmse	10.8007	3542.61
	4_DecisionTree	Decision Tree	rmse	10.8007	3568.62
	5_Default_LightGBM	LightGBM	rmse	0.611488	5923.24
	6_Default_Xgboost	Xgboost	rmse	0.633721	5824.88
	7_Default_CatBoost	CatBoost	rmse	0.346605	41347
	8_Default_NeuralNetwork	Neural Network	rmse	1.50702	4638.86
	9_Default_RandomForest	Random Forest	rmse	9.83145	4801.5
	10_Default_ExtraTrees	Extra Trees	rmse	10.4325	4744.49
	20_LightGBM	LightGBM	rmse	0.588545	6264.59
	11_Xgboost	Xgboost	rmse	0.720779	5940.07
	29_CatBoost	CatBoost	rmse	0.358175	60123.8
	38_RandomForest	Random Forest	rmse	9.28304	5999.16
	47_ExtraTrees	Extra Trees	rmse	11.5642	5532.58
	56_NeuralNetwork	Neural Network	rmse	1.76989	5756.18
	6_Default_Xgboost_categorical_mix	Xgboost	rmse	0.625617	7511.86
	20_LightGBM_GoldenFeatures	LightGBM	rmse	0.521862	6676.63
	5_Default_LightGBM_GoldenFeatures	LightGBM	rmse	0.56261	7515.12
	6_Default_Xgboost_categorical_mix_GoldenFeatures	Xgboost	rmse	0.574645	8348.49
	20_LightGBM_KMeansFeatures	LightGBM	rmse	0.611778	6887.65
	57_CatBoost	CatBoost	rmse	0.509914	30362.9
	58_CatBoost	CatBoost	rmse	0.515477	47150.4
	57_CatBoost_BoostOnErrors	CatBoost	rmse	0.516569	31113.8
	Ensemble	Ensemble	rmse	0.324031	2.36
	7_Default_CatBoost_Stacked	CatBoost	rmse	0.444808	20187.4
	20_LightGBM_GoldenFeatures_Stacked	LightGBM	rmse	0.482133	6414.12
	6_Default_Xgboost_categorical_mix_GoldenFeatures_Stacked	Xgboost	rmse	0.473802	6722.89
the best	Ensemble_Stacked	Ensemble	rmse	0.321204	2.98

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