häviöfunktions

A häviöfunktio, often translated as loss function or cost function, is a fundamental concept in machine learning and optimization. It quantifies the difference between the predicted output of a model and the actual target value. In essence, it measures how "wrong" a model's prediction is. The goal of training a machine learning model is to minimize this häviöfunktio.

The specific form of the häviöfunktio depends on the type of problem being solved. For regression tasks, where the model predicts a continuous value, common häviöfunktiot include Mean Squared Error (MSE), which calculates the average of the squared differences between predicted and actual values, and Mean Absolute Error (MAE), which uses the average of the absolute differences.

For classification tasks, where the model predicts a discrete category, häviöfunktiot 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. A lower häviöfunktio indicates that the model is performing better, as its predictions are closer to the true values.

Optimization algorithms, such as gradient descent, are employed to iteratively adjust the model's parameters in order to reduce the value of the häviöfunktio. By minimizing the häviöfunktio, the model learns to make more accurate predictions. The choice of häviöfunktio is crucial as it directly influences the model's learning process and its ultimate performance.