ülefitseerimist

ülefitseerimist refers to the concept of over-fitting in machine learning. It occurs when a model learns the training data too well, including its noise and random fluctuations. As a result, the model performs poorly on new, unseen data because it has not generalized effectively. This means the model has essentially memorized the training set rather than identifying the underlying patterns.

Over-fitting can happen for several reasons. If a model is too complex relative to the amount of training data, it has more capacity to memorize the data. Insufficient training data can also contribute to over-fitting, as the model may not be exposed to enough examples to learn robust patterns. Additionally, training for too long can lead to over-fitting, where the model continues to adjust itself to fit even the most minor details of the training set.

Detecting over-fitting typically involves splitting the data into training and validation sets. The model is trained on the training set, and its performance is evaluated on the validation set. If the performance on the training set continues to improve while the performance on the validation set starts to degrade, it indicates over-fitting.

Several techniques can be employed to combat over-fitting. Regularization methods, such as L1 and L2 regularization, add a penalty to the model's complexity, discouraging it from becoming too intricate. Increasing the amount of training data or using data augmentation techniques can also help. Simplifying the model by reducing the number of parameters or layers can also mitigate over-fitting. Early stopping, which involves halting the training process when performance on the validation set begins to worsen, is another effective strategy.