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Kaotusefunktsioonid, known in English as loss functions, are mathematical tools used in machine learning to quantify the difference between predicted values and actual values. They play a pivotal role in guiding the training process of models such as neural networks, support vector machines, and regression algorithms. By providing a scalar measure of error, a loss function allows optimization algorithms, typically gradient descent, to adjust model parameters to minimize prediction error.

Common continuous loss functions include mean squared error (MSE) for regression tasks, which squares the difference between predicted and actual values, and absolute error, which evaluates the absolute difference. In classification, cross‑entropy loss is widely used because it measures the divergence between predicted probabilities and true class labels, promoting confident and accurate predictions. Another frequently applied loss is the hinge loss, primarily associated with support vector machines, which encourages correct classification with a margin.

Choosing an appropriate loss function depends on task requirements and data characteristics. Robust loss functions, such as Huber loss, mitigate the influence of outliers by combining squared and absolute error behaviors. For tasks involving class imbalance, focal loss focuses training on hard-to-classify examples by down‑weighting well‑predicted instances. Additionally, custom loss functions can be created to embed domain‑specific constraints or business objectives.

In practice, loss functions are incorporated into a model’s objective, which optimizers seek to minimize. The steeper the loss landscape, the faster potential convergence, but highly complex losses may introduce optimization challenges. Proper selection, implementation, and tuning of loss functions are essential for achieving models that generalize well to unseen data.