Summary of model base parameters

Import libraries and read data

import pandas as pd
data = pd.read_csv('output/performance_base.csv')

Get descriptive statistics, and save

stats = data.describe().T
stats.to_csv('./output/base_parameters_stats.csv')

stats

	count	mean	std	min	25%	50%	75%	max
thrombolysis_rate	132.0	0.114536	0.035449	0.015306	0.093239	0.109547	0.133314	0.243174
admissions	132.0	572.777778	277.416470	101.000000	377.666667	544.166667	755.083333	2039.333333
80_plus	132.0	0.425635	0.056416	0.292308	0.387089	0.427573	0.457896	0.576220
onset_known	132.0	0.665679	0.134064	0.344784	0.573593	0.641978	0.752881	0.988275
known_arrival_within_4hrs	132.0	0.600743	0.089733	0.257034	0.557876	0.608542	0.662325	0.813584
onset_arrival_mins_mu	132.0	4.571655	0.098500	3.767890	4.530071	4.579801	4.628630	4.763117
onset_arrival_mins_sigma	132.0	0.559729	0.121209	0.435067	0.516637	0.547310	0.585710	1.800721
scan_within_4_hrs	132.0	0.947503	0.030163	0.847328	0.932828	0.953808	0.969191	1.000000
arrival_scan_arrival_mins_mu	132.0	3.279822	0.409251	1.665700	3.029675	3.309899	3.572156	4.235924
arrival_scan_arrival_mins_sigma	132.0	0.941438	0.220601	0.549301	0.787521	0.890961	1.034230	1.751363
onset_scan_4_hrs	132.0	0.874621	0.043245	0.627178	0.852508	0.876833	0.904059	0.962230
eligable	132.0	0.338312	0.082637	0.111111	0.276355	0.331651	0.398860	0.531940
scan_needle_mins_mu	132.0	3.425993	0.342337	2.572714	3.204064	3.426290	3.676980	4.408589
scan_needle_mins_sigma	132.0	0.689888	0.156039	0.366017	0.579162	0.661771	0.793168	1.282829