Also known as: K fold Cross Validation
K-блочная кросс-валидация — метод оценки модели, который делит набор данных на k частей и обучает и оценивает модель k раз. Каждая часть один раз используется как валидационная выборка, а остальные части — для обучения. Результаты усредняются, чтобы более устойчиво оценить способность к обобщению.
An algorithm for predicting a model's ability to generalize to new data. The k in k-fold refers to the number of equal groups you divide a dataset's examples into; that is, you train and test your model k times. For each round of training and testing, a different group is the test set, and all remaining groups become the training set. After k rounds of training and testing, you calculate the mean and standard deviation of the chosen test metric(s). For example, suppose your dataset consists of 120 examples. Further suppose, you decide to set k to 4. Therefore, after shuffling the examples, you divide the dataset into four equal groups of 30 examples and conduct four training and testing rounds: !A dataset broken into four equal groups of examples. In Round 1, the first three groups are used for training and the last group is used for testing. In Round 2, the first two groups and the last group are used for training, while the third group is used for testing. In Round 3, the first group and the last two groups are used for training, while the second group is used for testing. In Round 4, the first group is used is for testing, while the final three groups are used for training. For example, Mean Squared Error (MSE) might be the most meaningful metric for a linear regression model. Therefore, you would find the mean and standard deviation of the MSE across all four rounds.