ylisovittuvuuteen

Ylisovittuvuuteen, a Finnish term, 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, to the point where it captures noise and random fluctuations in the data as if they were genuine patterns. This results in a model that performs exceptionally well on the data it was trained on, but poorly on new, unseen data.

The primary cause of overfitting is a model that is too complex relative to the amount of data available. For instance, a polynomial regression with a very high degree might perfectly fit a small set of data points, including any slight deviations. However, when presented with a new data point that doesn't lie precisely on that complex curve, the prediction will likely be inaccurate. Another factor can be training for too long, allowing the model to progressively memorize the training examples rather than generalize from them.

Detecting overfitting typically involves splitting the data into training and testing sets. The model is trained on the training set, and its performance is then evaluated on the unseen testing set. A significant disparity between performance on the training set and the testing set is a strong indicator of overfitting.

To combat overfitting, several techniques are employed. These include using simpler models, increasing the amount of training data, applying regularization methods (which penalize model complexity), and employing cross-validation. The goal is always to build a model that generalizes well to new, unseen data, ensuring its practical utility.