virhekäyriä

Virhekäyriä, meaning "error curves" in Finnish, refers to visualizations used in various fields, particularly in machine learning and statistics, to represent the performance of a model or algorithm. These curves typically plot a performance metric against a varying parameter or threshold. The most common types of error curves include ROC curves and precision-recall curves.

A Receiver Operating Characteristic (ROC) curve plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings. It helps to visualize the trade-off between correctly identifying positive instances and incorrectly classifying negative instances. An ideal ROC curve would rise steeply and then flatten out horizontally, indicating high sensitivity and low specificity. The Area Under the Curve (AUC) is a common metric derived from the ROC curve, summarizing its overall performance.

Precision-recall curves, on the other hand, plot precision (the proportion of true positive predictions among all positive predictions) against recall (the proportion of true positive predictions among all actual positive instances). These curves are particularly useful for imbalanced datasets where the number of negative instances significantly outweighs the number of positive instances. A good precision-recall curve would show high precision and high recall simultaneously.

The interpretation of error curves allows for informed decisions regarding model selection, hyperparameter tuning, and threshold optimization. By analyzing the shape and position of these curves, practitioners can gain insights into a model's strengths and weaknesses and make adjustments to improve its predictive power. The specific metric chosen for the y-axis depends on the problem's context and the relative importance of different types of errors.