SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 24: Full output of the linear model assessing the impact of data-centric processing on the feature selection task on the label noise data with interaction effects. The model has the following formula: spearmans_r  $\sim$  generative_model + perc_label_noise * (preprocessing_strategy + postprocessing_strategy).
coef	std err	t	P> |t|	[0.025, 0.975]
Intercept	0.4679	0.033	14.099	0.000	0.403	0.533
generative_model[Tctgan]	0.1575	0.020	7.887	0.000	0.118	0.197
generative_model[Ttdpm]	-0.1104	0.020	-5.530	0.000	-0.150	-0.071
generative_model[Ttaff]	-0.1318	0.020	-6.602	0.000	-0.111	-0.083
generative_model[Ttva]	0.1575	0.020	7.889	0.000	0.118	0.197
perLabelNoise[T2]	-0.0417	0.043	-0.961	0.337	-0.127	0.044
perLabelNoise[T4]	-0.0252	0.043	-0.581	0.561	-0.110	0.060
perLabelNoise[T6]	-0.0337	0.043	-0.777	0.437	-0.119	0.051
perLabelNoise[T8]	0.0183	0.043	-0.067	0.103	-0.422	-0.067
perLabelNoise[T10]	-0.0631	0.044	-1.441	0.150	-0.149	0.023
perPost[ThBaseline - no-hard]	-0.0016	0.043	-0.036	0.971	-0.087	0.084
perPost[Teasy-hard - baseline]	0.0633	0.043	1.459	0.145	-0.022	0.148
perPost[Teasy-hard - no-hard]	0.0637	0.043	1.469	0.142	-0.021	0.149
perLabelNoise[T2]	0.0491	0.043	0.811	0.111	-0.111	0.111
perLabelNoise[T4]	-0.0089	0.061	-0.145	0.885	-0.129	0.112
perLabelNoise[T6]	-0.0042	0.061	-0.068	0.946	-0.125	0.116
perLabelNoise[T8]	-0.0362	0.061	-0.590	0.556	-0.157	0.084
perLabelNoise[T10]	0.0369	0.062	0.595	0.552	-0.085	0.159
perLabelNoise[T2]	0.0641	0.061	1.044	0.297	-0.217	0.185
perLabelNoise[T4]	-0.0078	0.061	-0.127	0.899	-0.128	0.113
perLabelNoise[T6]	-0.0216	0.061	-0.352	0.725	-0.142	0.099
perLabelNoise[T8]	-0.0944	0.061	-1.539	0.124	-0.215	0.026
perLabelNoise[T10]	-0.0312	0.062	-0.504	0.614	-0.153	0.090
perLabelNoise[T2]	-0.0521	0.061	0.850	0.396	-0.173	0.173
perLabelNoise[T4]	-0.0358	0.061	-0.584	0.559	-0.156	0.085
perLabelNoise[T6]	-0.0449	0.061	-0.733	0.464	-0.165	0.076
perLabelNoise[T8]	-0.0846	0.061	-1.380	0.168	-0.205	0.036
perLabelNoise[T10]	-0.0637	0.062	-1.029	0.304	-0.185	0.058
Table 25: Full output of the linear model assessing the impact of data-centric processing on the model selection task on the label noise data with interaction effects. The model has the following formula: spearmans_r  $\sim$  generative_model + perc_label_noise * (preprocessing_strategy + postprocessing_strategy).
coef	std err	t2	P> |t|	[0.025, 0.975]
Intercept	0.4914	0.049	10.055	0.000	0.395	0.587
generative_model[Tetgan]	0.0166	0.029	0.563	0.574	-0.041	0.074
generative_model[Ttdpim]	-0.1090	0.029	-3.709	0.000	-0.167	-0.051
generative_model[Ttaffom]	0.0257	0.029	0.873	0.383	0.383	0.083
generative_model[Ttva]	-0.0138	0.029	-0.469	0.639	-0.072	0.044
perLabel_label[T2]	-0.0162	0.064	-0.254	0.800	-0.142	0.109
perLabel_label[T4]	-0.0781	0.064	-1.223	0.222	-0.204	0.047
perLabel_label[T6]	-0.0867	0.064	-1.357	0.175	-0.212	0.039
perLabel_label[T8]	-0.1581	0.064	-2.476	0.014	-0.033	0.033
perLabel_label[T10]	-0.2077	0.065	-3.219	0.001	-0.334	-0.081
perPost[Tbaserine-no-hard]	0.1819	0.064	2.849	0.005	0.056	0.307
perPost[Tcexy-hard-baseline]	0.1057	0.064	1.656	0.098	-0.020	0.231	Omnibus: 1.138	Durbin-Watson: 1.634
Method: Least Squares	F-statistic: 7.422	0.2276	0.064	3.565	0.000	0.102	0.353	Prob(Omnibus): 0.566	Jargae-Bera (JB): 1.224
Date: Thu, 10 Aug 2023	Prob(F-statistic): 1.12e-23	-0.315	-1.55e-23	-1.51e-23	0.127	-0.212	-0.041	Skev: 0.077	Prob(JB): 0.542
Time: 14:32:11	Log-Likelihood: -55.698	-0.1467	-0.090	-1.624	0.105	-0.324	0.031	Kurtosis: 2.840	Cond. No.: 34.7
No. Observations: 596	AIC: -55.40	-0.0343	0.090	-0.380	0.704	-0.212	0.143
DF Residuals: 568	BIC: 67.53	-0.1562	0.090	-1.730	0.084	-0.334	0.021
DF Model: 27 nonrobust	-0.1531	0.091	-1.678	0.094	-0.332	0.026
Covariance Type:	-0.1305	0.090	-1.445	0.149	-0.308	0.047
perLabel_label[T8]	-0.1410	0.090	-1.561	0.119	-0.318	0.036
perLabel_label[T9]	-0.0648	0.090	-0.717	0.474	-0.242	0.113
perLabel_label[T8]	-0.0667	0.090	-0.738	0.461	-0.244	0.111
perLabel_label[T9]	-0.0531	0.091	-0.582	0.561	-0.232	0.126
perLabel_label[T8]	-0.0886	0.090	-0.886	0.376	-0.237	0.107
perLabel_label[T4]	-0.0257	0.090	-0.285	0.776	-0.203	0.152
perLabel_label[T6]	-0.0190	0.090	-0.211	0.833	-0.196	0.158
perLabel_label[T8]	-0.1933	0.090	-2.141	0.033	-0.371	-0.016
perLabel_label[T9]	-0.1681	0.091	-1.842	0.066	-0.347	0.011