Ylisovittamista

Ylisovittamista is a Finnish term that translates to "overfitting" in English. It is a concept commonly encountered in statistics and machine learning. Overfitting occurs when a statistical model learns the training data too well, to the point that it captures the noise and random fluctuations in the data rather than the underlying general patterns. This leads to a model that performs very well on the data it was trained on but poorly on new, unseen data.

The problem of overfitting arises when a model is too complex relative to the amount of data available, or when the training process continues for too long. In machine learning, this can happen with algorithms that have a high degree of flexibility, such as deep neural networks or decision trees with many branches. When a model overfits, its predictive accuracy on validation or test datasets significantly decreases compared to its accuracy on the training dataset.

To combat overfitting, several techniques are employed. These include using simpler models, collecting more training data, regularization methods (which penalize model complexity), cross-validation (to assess model performance on unseen data), and early stopping (halting the training process before the model starts to overfit). Identifying and mitigating overfitting is crucial for building models that generalize well to new situations and provide reliable predictions.