krossivalidoinnin

Krossivalidoinnin, or cross-validation in English, is a resampling technique used to evaluate machine learning models on a limited data sample. It helps in understanding how the model will generalize to an independent dataset. The core idea is to split the available dataset into multiple subsets, called folds.

In k-fold cross-validation, the dataset is divided into k equal-sized folds. The model is then trained k times. In each iteration, one of the folds is used as the testing set, and the remaining k-1 folds are used for training. This process ensures that every data point gets to be in the testing set exactly once. After k iterations, the performance metrics (such as accuracy, precision, recall, or mean squared error) from each iteration are averaged to provide a more robust estimate of the model's performance.

Common values for k include 5 and 10. A higher k generally leads to a more accurate estimate of the model's generalization ability, but it also increases the computational cost as the model needs to be trained more times. A lower k might result in a more biased estimate because the training sets are smaller or the test sets are larger.

Cross-validation is particularly useful when the dataset is small, as it allows for a more thorough evaluation of the model's performance without needing to set aside a large portion of the data for a separate validation set, which would reduce the amount of data available for training. It helps to identify if a model is overfitting or underfitting. Overfitting occurs when a model performs very well on the training data but poorly on unseen data, while underfitting happens when the model fails to capture the underlying patterns in the data.