kfoldristipäivityksen
kfoldristipäivityksen is a Finnish term that translates to "k-fold cross-validation update" or "k-fold cross-validation refinement". It refers to a process within machine learning where the k-fold cross-validation technique is iterated or modified to improve model performance or understanding. This typically involves re-running the cross-validation with adjusted parameters, different data subsets, or a modified model architecture.
The core idea behind k-fold cross-validation is to divide the dataset into 'k' equal subsets. The model
For instance, after an initial k-fold cross-validation yields a certain accuracy, a data scientist might perform