Validation Strategies for Predicted Results #27
Description
Hello,
Thank you so much for this amazing pipeline! I am relatively new to this pipeline and wanted to ask about some strategies in terms of validating the imputed results. We will run our data with 3 models - Neural net, SVG and XGboost to compare which fits best with the data. Just wondering if there's any QC tools/metrics or some sort of scoring system to guide the assessment of whether the prediction is robust quantitatively and how predictions by each model compare against each other for every marker.
Look forward to discussing with you, thanks!