What are classification metrics used for?

Classification metrics are essential tools used to evaluate the performance of classification models. They provide quantitative measures that help assess how well the model predicts the classes in a given dataset. These metrics include accuracy, precision, recall, F1 score, and ROC-AUC, among others. By utilizing these metrics, data scientists can determine how effectively the model is able to distinguish between different categories based on the data provided.

Understanding classification metrics enables professionals to make informed decisions about model selection, tuning, and improvement. It helps in identifying model strengths and weaknesses in different scenarios, which is crucial for developing robust machine learning applications. The focus on performance evaluation through classification metrics allows practitioners to refine their models and achieve better predictive accuracy in real-world applications.