"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File ../osic-pulmonary-fibrosis-progression/test.csv does not exist: '../osic-pulmonary-fibrosis-progression/test.csv'"
We will preduct, for each patient the test set, their FVC for the weeks that are in the sample set for them (this is a requirement from the competition):
"For each Patient_Week from the submission file, you must predict the FVC and a confidence"
This competition is evaluated on a modified version of the Laplace Log Likelihood. In medical applications, it is useful to evaluate a model's confidence in its decisions. Accordingly, the metric is designed to reflect both the accuracy and certainty of each prediction.For each true FVC measurement, you will predict both an FVC and a confidence measure (standard deviation $\sigma$). The metric is computed as:
$\sigma_{clipped} = max(\sigma, 70)$,
$\Delta = min ( |FVC_{true} - FVC_{predicted}|, 1000 )$
The final score is calculated by averaging the metric across all test set Patient_Weeks (three per patient). Note that metric values will be negative and higher is better.
