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    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.

    The optimal number of workers will then depend on your dataset size, number of cores, and available system memory. This modal provides all functionality needed to create and open an Xcessiv project.You can use either set to 1 for this functionality. If you click again on Calculate Extracted Datasets Statistics, you will notice that the base learner cross-validation statistics will show you the number of splits generated.Since most problems will rely on very common cross-validation methods, Xcessiv provides several preset Since the secondary learner of a stacked ensemble is trained on a different set of features (the meta-features), it is natural to define a separate cross-validation method for it.is the Numpy array corresponding to the ground truth labels of each sample.Experienced scikit-learn users will recognize this format as the one accepted by scikit-learn estimators.

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