odavusfunktsioonid

Odavusfunktsioonid, also known as cost functions or loss functions, are mathematical expressions used in machine learning and optimization to quantify the error or discrepancy between predicted values and actual values. The goal of many machine learning algorithms is to minimize this odavusfunktsioon. A lower odavusfunktsioon value indicates a better performing model. The specific odavusfunktsioon chosen depends on the type of problem being solved.

For regression tasks, where the goal is to predict a continuous value, common odavusfunktsioonid include Mean Squared Error (MSE) and Mean Absolute Error (MAE). MSE calculates the average of the squared differences between predicted and actual values, penalizing larger errors more heavily. MAE, on the other hand, calculates the average of the absolute differences, providing a more linear penalty.

In classification problems, where the goal is to assign data points to discrete categories, odavusfunktsioonid like Cross-Entropy Loss are frequently used. Cross-Entropy measures the performance of a classification model whose output is a probability value between 0 and 1. It quantizes the difference between the predicted probability distribution and the true distribution. The choice of odavusfunktsioon significantly impacts how an algorithm learns and the final performance of the model.