yliviritä

Ylivirittä is a Finnish term that translates to "overfitting" in English and refers to a common problem in machine learning and statistical modeling. It occurs when a model learns the training data too well, including its noise and random fluctuations, to the point where it performs poorly on new, unseen data. Essentially, the model has become too specific to the training set and has lost its ability to generalize.

This phenomenon can arise from various factors, such as using a model that is too complex for the amount of data available, training for too long, or having insufficient or noisy training data. When a model overfits, its accuracy on the training data might be very high, but its performance on a separate test set or in real-world applications will be significantly lower.

To combat overfitting, several techniques are employed. These include using simpler models, increasing the size of the training dataset, employing regularization methods that penalize model complexity, cross-validation to evaluate performance on unseen data, and early stopping during the training process. Identifying and mitigating ylivirittä is crucial for building robust and reliable machine learning models that can effectively make predictions on new data.