DNAttend

DNAttend trains two models independently; a baseline logistic regression model and a CatBoost model. The CatBoost model is trained via a cross-validated randomised hyper-parameter search with over-fit detection. In addition, over-fit detection is performed using a holdout validation set to determine the optimal boosting iterations.

Output probability of both models are calibrated via cross-validation.

Finally, decision thresholds are tuned, using the training dataset, to optimise either the ROC or F1 score.

You can find the code here

RAP level: Silver

A view of the Code documentation can be seen in the iframe below.