kryssfeltvalidering

Kryssfeltvalidering, also known as cross-validation, is a statistical technique used to evaluate the performance of a machine learning model. It is particularly useful for assessing how well a model will generalize to new, unseen data. The core idea is to divide the available dataset into multiple subsets.

A common method is k-fold cross-validation. In this approach, the dataset is randomly partitioned into k equal-sized folds. The model is then trained k times. In each iteration, one fold is held out as the validation set, and the remaining k-1 folds are used for training. This process ensures that every data point gets to be in a validation set exactly once.

After training and validating on all k folds, the performance metrics (such as accuracy, precision, or recall) from each fold are averaged. This average provides a more robust estimate of the model's performance than a single train-test split, as it reduces the impact of randomness in how the data was divided.

Cross-validation helps to detect overfitting, a situation where a model performs very well on the training data but poorly on new data. By using unseen data for validation, it gives a clearer picture of the model's predictive power. The choice of k is a hyperparameter; typical values range from 5 to 10. A higher k generally leads to a more reliable estimate but requires more computational resources.