Confusion Matrix Made Simple: Accuracy, Precision, Recall: F1-Score

This article explains the confusion matrix and related classification metrics like accuracy, precision, recall, and F1-score. It uses the Wisconsin Breast Cancer Dataset to demonstrate how to build a classification model and evaluate its performance. The article explains how to calculate these metrics and why they are important in different contexts, especially in healthcare applications. It also discusses the importance of considering different metrics based on the data's context and how to adjust thresholds to optimize the model's performance.