Odavusfunktsioon

Odavusfunktsioon, often translated as "cost function" or "loss function" in English, is a fundamental concept in optimization and machine learning. It quantifies the discrepancy between a predicted outcome and the actual or desired outcome. The goal in many optimization problems is to minimize this function.

In machine learning, an odavusfunktsioon is used to train models. During training, the model makes predictions, and the odavusfunktsioon evaluates how far off these predictions are. This error signal is then used by an optimization algorithm, such as gradient descent, to adjust the model's parameters in an iterative process, aiming to reduce the odavusfunktsioon's value. A lower odavusfunktsioon value generally indicates a better-performing model.

Various types of odavusfunktsioonid exist, chosen based on the specific problem. For regression tasks, common examples include Mean Squared Error (MSE) and Mean Absolute Error (MAE). For classification problems, Cross-Entropy is frequently employed. The selection of an appropriate odavusfunktsioon is crucial for the effective training and performance of a machine learning model. It guides the learning process by defining what constitutes an "error" and how to rectify it.