Validating processe thrugh walkthroughs
Again, this is a concept taken straight out of the scikit-learn API and as such, most built-in cross-validation iterators from scikit-learn will work. The given code does stratified K-Fold validation with 5 train-test splits and a random seed set at 8.So far, we have given code for defining cross-validation.Since Xcessiv Projects is an empty folder, we won’t see any existing projects yet. Now would be a good time to explain the structure of an Xcessiv project.
Experienced scikit-learn users will recognize this format as the one accepted by scikit-learn estimators.As long as After defining the main dataset, we must define how Xcessiv does cross-validation for its base learners.In Xcessiv, the base learner cross-validation’s purpose is two-fold.First, the cross-validation method is used to calculate the model-hyperparameter combination’s relevant evaluation metrics on the data.
Experienced users will recognize this as the usual purpose of cross-validation in machine learning.
First, make sure your Redis server is up and running.