Loghäviötä

Loghäviötä is a Finnish term that translates to "log loss" or "logarithmic loss" in English. It is a performance metric used in machine learning, particularly for classification tasks. Log loss quantifies the performance of a classification model where the prediction is a probability value between 0 and 1.

The formula for log loss is derived from the cross-entropy function. For a binary classification problem with a true label y (0 or 1) and a predicted probability p, the log loss for a single instance is calculated as -(y * log(p) + (1-y) * log(1-p)). A lower log loss value indicates a better performing model, as it means the predicted probabilities are closer to the actual outcomes.

Log loss penalizes incorrect predictions more heavily than correct ones. For example, if a model predicts a probability of 0.1 for an instance that is actually 1, the log loss will be significantly higher than if it had predicted 0.9. This property makes log loss a useful metric when the cost of misclassification is high or when the model's confidence in its predictions is important.

It is commonly used in scenarios such as spam detection, medical diagnosis, and credit scoring, where accurate probability estimates are crucial for decision-making. Log loss is applicable to both binary and multi-class classification problems. For multi-class problems, the calculation involves summing the log loss for each class, weighted by the true probability of that class.