Hi, when using nested cross validation for hyper parameter search/performance estimation, it's not clear how the best hyper parameters are chosen?
As in nested cv, we can actually have different "best parameters" by outer fold (ie, not stable hyper params), how does dataiku selects and reports the best one?
Using nested cross-validation you will train m different logistic regression models, 1 for each of the m outer folds, and the inner folds are used to optimize the hyperparameters of each model (e.g., using gridsearch in combination with k-fold cross-validation.
If your model is stable, these mmodels should all have the same hyperparameter values, and you report the average performance of this model based on the outer test folds. Then, you proceed with the next algorithm, e.g., an SVM etc.