Скачать или смотреть Understanding Precision and Recall Metrics

@genaiexp Let's start by defining precision and recall, two crucial metrics in evaluating machine learning models. Precision is the ratio of true positive results to the total number of positive predictions made by the model. It measures the accuracy of positive predictions, providing insight into how many of the predicted positive instances are actually correct. On the other hand, recall, also known as sensitivity, is the ratio of true positive results to the actual number of positive instances. It assesses the model's ability to identify all relevant instances in the dataset. While accuracy gives a general idea of the model's performance, precision and recall provide a more nuanced view, especially in datasets with imbalanced classes. In scenarios where the cost of false positives and false negatives varies significantly, optimizing these metrics becomes crucial. Understanding the formulae and implications of precision and recall is the first step towards mastering data-driven decision-making in machine learning.