k-Fold introduces a new way of splitting the dataset which helps to overcome the “test only once bottleneck”. K-Fold cross-validation is a technique that minimizes the disadvantages of the hold-out method. Due to the reasons mentioned before, the result obtained by the hold-out technique may be considered inaccurate. Moreover, the fact that we test our model only once might be a bottleneck for this method. Both training and test sets may differ a lot, one of them might be easier or harder. For example, the training set will not represent the test set. If so we may end up in a rough spot after the split. Still, hold-out has a major disadvantage.įor example, a dataset that is not completely even distribution-wise. ![]() X_train, X_test, y_train, y_test = train_test_split(X, y, import numpy as npįrom sklearn.model_selection import train_test_split For example, you may do it using sklearn.model_ain_test_split. We usually use the hold-out method on large datasets as it requires training the model only once. Usually, 80% of the dataset goes to the training set and 20% to the test set but you may choose any splitting that suits you better
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