Tappiofunktioita

Tappiofunktioita, known in English as loss functions or cost functions, are fundamental concepts in machine learning and optimization. They quantify the "cost" or "error" of a model's prediction compared to the actual target value. In essence, a loss function takes the predicted output of a model and the true target as input and outputs a single scalar value representing how bad the prediction was. The goal of training a machine learning model is to minimize this loss function.

Different types of loss functions are used depending on the problem at hand. For regression tasks, where the goal is to predict a continuous value, common loss functions include Mean Squared Error (MSE) and Mean Absolute Error (MAE). MSE calculates the average of the squared differences between predictions and actual values, heavily penalizing larger errors. MAE, on the other hand, computes the average of the absolute differences, making it less sensitive to outliers than MSE.

For classification problems, where the goal is to categorize data into discrete classes, loss functions like Cross-Entropy loss are frequently employed. Cross-entropy measures the difference between two probability distributions: the predicted probability distribution of classes and the true distribution. Binary Cross-Entropy is used for two-class problems, while Categorical Cross-Entropy is used for multi-class problems. The choice of loss function can significantly impact the performance and convergence of a machine learning model.