Datasets:

DaniFrame
/

AFRLA-instance-level-results

instance int64 11 4.17k	Length float64 0.08 0.78	Diameter float64 0.06 0.62	Height float64 0.01 0.52	Whole_weight float64 0 2.55	Shucked_weight float64 0 1.19	Viscera_weight float64 0 0.64	Shell_weight float64 0 1.01	Sex_F int64 0 1	Sex_I int64 0 1	Sex_M int64 0 1	real int64 1 26	prediction float64 2.67 27	model stringclasses 5 values	cpu_training_time int64 9M 27.1B	cpu_prediction_time int64 966k 167M	memory_usage int64 2.31k 71.4M	max_depth int64 3 11	learning_rate float64 -1 0.1	n_estimators int64 1 1k
11	0.43	0.35	0.11	0.406	0.1675	0.081	0.135	0	0	1	10	9.566029	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
14	0.47	0.355	0.1	0.4755	0.1675	0.0805	0.185	1	0	0	10	9.978592	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
18	0.365	0.295	0.08	0.2555	0.097	0.043	0.1	0	0	1	7	9.110844	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
19	0.45	0.32	0.1	0.381	0.1705	0.075	0.115	0	0	1	9	9.482685	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
23	0.55	0.415	0.135	0.7635	0.318	0.21	0.2	1	0	0	9	9.942024	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
29	0.575	0.425	0.14	0.8635	0.393	0.227	0.2	0	0	1	11	9.919127	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
36	0.54	0.475	0.155	1.217	0.5305	0.3075	0.34	1	0	0	16	10.493841	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
39	0.355	0.29	0.09	0.3275	0.134	0.086	0.09	0	0	1	9	8.968534	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
41	0.55	0.425	0.135	0.8515	0.362	0.196	0.27	1	0	0	14	10.875035	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
45	0.39	0.295	0.095	0.203	0.0875	0.045	0.075	0	1	0	7	8.222986	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
52	0.485	0.36	0.13	0.5415	0.2595	0.096	0.16	0	0	1	10	9.784402	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
55	0.5	0.4	0.14	0.6615	0.2565	0.1755	0.22	1	0	0	8	10.62645	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
64	0.52	0.4	0.12	0.58	0.234	0.1315	0.185	0	0	1	8	9.961788	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
76	0.595	0.475	0.14	0.944	0.3625	0.189	0.315	0	0	1	9	11.496401	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
77	0.6	0.47	0.15	0.922	0.363	0.194	0.305	1	0	0	10	11.15231	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
82	0.52	0.425	0.165	0.9885	0.396	0.225	0.32	1	0	0	16	11.656438	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
87	0.56	0.44	0.16	0.8645	0.3305	0.2075	0.26	0	0	1	10	10.875035	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
88	0.46	0.355	0.13	0.517	0.2205	0.114	0.165	1	0	0	9	9.887986	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
102	0.53	0.435	0.16	0.883	0.316	0.164	0.335	0	0	1	15	11.774385	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
104	0.605	0.47	0.16	1.1735	0.4975	0.2405	0.345	0	0	1	12	10.514009	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
112	0.435	0.32	0.08	0.3325	0.1485	0.0635	0.105	0	1	0	9	8.299627	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
113	0.425	0.35	0.105	0.393	0.13	0.063	0.165	0	0	1	9	9.887986	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
122	0.515	0.425	0.14	0.766	0.304	0.1725	0.255	1	0	0	14	10.888725	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
133	0.35	0.26	0.095	0.211	0.086	0.056	0.068	0	1	0	7	8.222986	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
136	0.305	0.23	0.08	0.156	0.0675	0.0345	0.048	1	0	0	7	8.05049	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
142	0.65	0.52	0.19	1.3445	0.519	0.306	0.4465	0	0	1	16	11.913371	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
143	0.56	0.455	0.155	0.797	0.34	0.19	0.2425	0	0	1	11	10.481428	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
160	0.605	0.465	0.165	1.056	0.4215	0.2475	0.34	0	0	1	13	11.051547	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
163	0.725	0.56	0.21	2.141	0.65	0.398	1.005	1	0	0	18	12.38953	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
171	0.53	0.395	0.145	0.775	0.308	0.169	0.255	1	0	0	7	10.859866	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
174	0.235	0.16	0.04	0.048	0.0185	0.018	0.015	0	1	0	5	6.762648	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
177	0.315	0.245	0.085	0.1435	0.053	0.0475	0.05	0	1	0	8	7.908025	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
193	0.355	0.275	0.085	0.22	0.092	0.06	0.15	0	1	0	8	8.87137	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
198	0.56	0.45	0.16	0.922	0.432	0.178	0.26	0	0	1	15	10.369246	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
208	0.525	0.415	0.17	0.8325	0.2755	0.1685	0.31	1	0	0	13	11.581014	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
229	0.53	0.415	0.16	0.783	0.2935	0.158	0.245	1	0	0	15	10.628235	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
230	0.555	0.445	0.135	0.836	0.336	0.1625	0.275	0	0	1	13	10.875035	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
234	0.44	0.35	0.135	0.435	0.1815	0.083	0.125	0	1	0	12	8.937349	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
236	0.075	0.055	0.01	0.002	0.001	0.0005	0.0015	0	1	0	1	6.698105	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
245	0.35	0.26	0.085	0.174	0.0705	0.0345	0.06	0	1	0	10	8.158091	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
256	0.56	0.45	0.185	1.07	0.3805	0.175	0.41	0	0	1	19	12.796808	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
259	0.59	0.475	0.16	1.1015	0.4775	0.2555	0.295	1	0	0	13	10.290013	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
275	0.655	0.54	0.215	1.844	0.7425	0.327	0.585	0	0	1	22	12.355755	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
279	0.515	0.425	0.135	0.712	0.2665	0.1605	0.25	1	0	0	11	11.274063	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
284	0.515	0.38	0.175	0.9565	0.325	0.158	0.31	0	0	1	14	11.15231	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
294	0.6	0.495	0.195	1.0575	0.384	0.19	0.375	0	0	1	26	12.561763	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
300	0.405	0.305	0.095	0.3485	0.1455	0.0895	0.1	1	0	0	9	9.110844	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
301	0.54	0.435	0.175	0.892	0.322	0.174	0.335	1	0	0	13	11.656438	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
303	0.36	0.27	0.1	0.217	0.0885	0.0495	0.0715	0	0	1	6	8.968534	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
305	0.2	0.145	0.06	0.037	0.0125	0.0095	0.011	0	1	0	4	6.698105	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
307	0.645	0.515	0.24	1.5415	0.471	0.369	0.535	0	0	1	13	13.492581	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
308	0.55	0.41	0.125	0.7605	0.2505	0.1635	0.195	0	0	1	14	10.62645	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
319	0.33	0.255	0.095	0.172	0.066	0.0255	0.06	0	1	0	6	8.158091	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
321	0.19	0.145	0.04	0.038	0.0165	0.0065	0.015	0	1	0	4	6.719865	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
329	0.36	0.28	0.09	0.2255	0.0885	0.04	0.09	0	1	0	8	8.222986	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
330	0.5	0.38	0.155	0.5955	0.2135	0.161	0.2	0	0	1	12	10.672597	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
334	0.74	0.6	0.195	1.974	0.598	0.4085	0.71	1	0	0	16	12.709539	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
338	0.62	0.475	0.185	1.325	0.6045	0.325	0.33	0	0	1	13	10.493841	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
342	0.62	0.465	0.185	1.274	0.579	0.3065	0.32	0	0	1	12	10.493841	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
351	0.585	0.45	0.17	0.8685	0.3325	0.1635	0.27	1	0	0	22	10.875035	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
354	0.635	0.515	0.17	1.275	0.509	0.286	0.34	0	0	1	16	10.493841	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
357	0.645	0.525	0.19	1.8085	0.7035	0.3885	0.395	1	0	0	18	10.861433	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
361	0.59	0.465	0.15	0.997	0.392	0.246	0.34	1	0	0	12	11.656438	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
363	0.6	0.48	0.15	1.029	0.4085	0.2705	0.295	1	0	0	16	10.6552	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
365	0.63	0.515	0.16	1.016	0.4215	0.244	0.355	0	0	1	19	11.106463	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
370	0.65	0.545	0.165	1.566	0.6645	0.3455	0.415	1	0	0	16	10.973206	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
373	0.7	0.575	0.17	1.31	0.5095	0.314	0.42	1	0	0	14	11.50098	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
375	0.675	0.545	0.195	1.7345	0.6845	0.3695	0.605	1	0	0	20	11.868217	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
378	0.565	0.465	0.175	0.995	0.3895	0.183	0.37	0	0	1	15	11.982457	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
382	0.485	0.4	0.135	0.663	0.313	0.137	0.2	0	0	1	10	10.059972	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
390	0.415	0.325	0.1	0.3215	0.1535	0.0595	0.105	0	1	0	10	8.350406	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
391	0.475	0.375	0.125	0.593	0.277	0.115	0.18	0	0	1	10	9.720616	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
392	0.47	0.375	0.125	0.5615	0.252	0.137	0.18	1	0	0	10	9.915641	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
395	0.39	0.31	0.1	0.302	0.116	0.064	0.115	0	1	0	11	8.808225	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
396	0.5	0.395	0.14	0.7155	0.3165	0.176	0.24	1	0	0	10	10.481428	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
399	0.585	0.455	0.15	0.987	0.4355	0.2075	0.31	0	0	1	11	10.582765	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
410	0.59	0.5	0.165	1.1045	0.4565	0.2425	0.34	0	0	1	15	10.524909	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
411	0.585	0.475	0.12	0.945	0.41	0.2115	0.28	0	0	1	14	10.369246	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
414	0.605	0.495	0.17	1.2385	0.528	0.2465	0.39	1	0	0	14	11.246863	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
418	0.63	0.5	0.155	1.005	0.367	0.199	0.36	1	0	0	16	11.982457	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
421	0.49	0.38	0.12	0.529	0.2165	0.139	0.155	0	1	0	11	9.008563	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
431	0.6	0.47	0.155	1.036	0.4375	0.196	0.325	0	0	1	20	10.879153	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
434	0.44	0.345	0.1	0.366	0.122	0.0905	0.12	0	1	0	13	8.850935	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
439	0.5	0.415	0.165	0.6885	0.249	0.138	0.25	0	0	1	13	11.29022	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
440	0.36	0.275	0.11	0.2335	0.095	0.0525	0.085	0	1	0	10	8.360179	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
456	0.64	0.54	0.175	1.221	0.51	0.259	0.39	1	0	0	15	11.246863	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
457	0.36	0.28	0.105	0.199	0.0695	0.045	0.08	0	1	0	9	8.360179	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
458	0.415	0.31	0.11	0.2965	0.123	0.057	0.0995	0	1	0	10	8.43682	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
509	0.56	0.445	0.155	0.8735	0.3005	0.209	0.275	0	1	0	16	10.992983	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
512	0.49	0.38	0.145	0.6725	0.249	0.181	0.21	1	0	0	10	10.642607	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
514	0.275	0.195	0.07	0.08	0.031	0.0215	0.025	1	0	0	5	7.568979	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
518	0.325	0.23	0.09	0.147	0.06	0.034	0.045	0	0	1	4	8.05049	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
521	0.36	0.27	0.09	0.1885	0.0845	0.0385	0.055	1	0	0	5	8.834942	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
531	0.46	0.355	0.13	0.458	0.192	0.1055	0.13	1	0	0	13	9.566029	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
536	0.52	0.405	0.14	0.5775	0.2	0.145	0.179	0	1	0	11	9.767941	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
542	0.42	0.325	0.115	0.2885	0.1	0.057	0.1135	0	0	1	15	9.523318	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
546	0.255	0.195	0.065	0.08	0.0315	0.018	0.027	0	0	1	8	7.170015	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
560	0.44	0.34	0.105	0.364	0.148	0.0805	0.1175	1	0	0	8	9.523318	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
565	0.35	0.255	0.065	0.179	0.0705	0.0385	0.06	0	0	1	10	8.834942	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100
567	0.435	0.33	0.125	0.406	0.1685	0.1055	0.096	0	1	0	12	8.43682	XGBRegressor	1,093,738,000	3,000,800	121,193	3	0.01	100

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Assessors For Regression: Loss Analysis - Instance Level Results

AFRLA - Instance Level Results is a collection of predictions at the instance/example level for eleven different regression tasks tested on 255 tree-based models (also called "base systems"). The aim of this dataset is to provide example-level results to train assessor models to predict performance of the tree-based models.

The dataset

The dataset presents eleven sections (one per regression task), with varying degrees of performance, difficulty and characteristics from the original tasks. Every one of the 255 models was trained on a subset of the dataset used for every task, and the results shown here are the test (never-before-seen by the models) predictions. Each subset has:

An instance identifier indicating the instance nº from the test set. This is just an identifier and it is not usually employed for training assessors, although in some occasions it may be useful for other analysis.
The original task features, the features used by the models to learn the task. Along with the instance identifier, they fully describe a test example.
The model features, descriptors of the 255 models. Mainly:
- The model used (XGBoost, Random Forest, Decision Tree...)
- Hyperparameters such as the maximum depth, number of estimators if applicable...
- Profiling metrics such as training time, inference time or memory usage
These metrics are not recorded per example, but rather per model (that is, if the inference time is 1.2 ms, the model predicted the entirety of the test dataset in that time, instead of just that example), and are then casted for each example. As such, they fully describre a model.

Partitions and versions

The sections are already partitioned into a predefined train-validation-test split for training assessors. Assessors need a particular kind of partitioning (mainly stratified by instance identifier to avoid contamination), so that's why the subsets are given.

The main branch contains the unaltered datasets, keeping the original values of the task and model characteristics, whereas the normalised branch contains the datasets properly normalised (numerical features are centered and scaled and categorical features are transformed into dummies).

Original tasks

Dataset	#Feat.	#Inst (test).	Cat.	Num.	Domain
Abalone	8	4177	Yes	Yes	Biology
Auction Verification	8	2043	Yes	Yes	Commerce
BNG EchoMonts	10	17496	Yes	Yes	Health
California Housing	8	20640	Yes	Yes	Real State
Infrared Thermography Temperature	33	1020	Yes	Yes	Health
Intrusion detection	4	182	No	Yes	Computer Science
Life Expectancy	21	2938	Yes	Yes	Health
Music Popularity	14	43597	Yes	Yes	Music
Parkinsons Telemonitoring (motor)	20	5875	No	Yes	Health
Parkinsons Telemonitoring (total)	20	5875	No	Yes	Health
Software Cost Estimation	6	145	Yes	Yes	Projects

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Size of downloaded dataset files:

604 MB

Size of the auto-converted Parquet files:

604 MB

Number of rows:

7,856,805