ristsvalideerimine

Ristvalideerimine, also known as cross-validation, is a statistical method used to estimate the performance of a machine learning model. It involves partitioning the data into subsets, training the model on some of these subsets, and validating it on the remaining subsets. This process is repeated multiple times to ensure that the model's performance is consistent and not dependent on a particular subset of the data.

There are several types of cross-validation techniques, including k-fold cross-validation, leave-one-out cross-validation, and stratified cross-validation. K-fold cross-validation is the most commonly used method, where the data is divided into k equally sized folds. The model is trained on k-1 folds and validated on the remaining fold. This process is repeated k times, with each fold serving as the validation set once. The final performance metric is the average of the k validation results.

Cross-validation is particularly useful in situations where the dataset is small, as it allows for more efficient use of the available data. It also helps to prevent overfitting, where a model performs well on training data but poorly on unseen data. By using cross-validation, researchers and practitioners can obtain a more accurate estimate of a model's performance and make more informed decisions about its suitability for a given task.