Häviöfunktion

A häviöfunktion, often translated as loss function or cost function, is a fundamental concept in machine learning and optimization. It quantifies the error or penalty associated with a model's predictions compared to the actual target values. The primary goal of training a machine learning model is to minimize this häviöfunktion.

The häviöfunktion takes the model's predictions and the true labels as input and outputs a single numerical value representing how "bad" the predictions are. A lower value indicates a better performing model, while a higher value signifies a poorer performance. Various types of häviöfunktiot exist, each suited for different types of problems. For regression tasks, common examples include Mean Squared Error (MSE) and Mean Absolute Error (MAE). For classification tasks, Cross-Entropy Loss is frequently used.

The process of minimizing the häviöfunktion typically involves optimization algorithms like gradient descent. These algorithms iteratively adjust the model's parameters (weights and biases) in the direction that reduces the häviöfunktion's output. The choice of an appropriate häviöfunktion is crucial as it directly influences the learning process and the final performance of the machine learning model. It guides the model towards learning the underlying patterns in the data by penalizing incorrect predictions.