Evaluation of machine learning models is crucial to determine their performance in real-world scenarios. Different evaluation metrics are appropriate based on the type of problem being solved. For classification tasks, accuracy is the most common metric, calculated as the number of correct predictions divided by the total predictions. Despite its simplicity, accuracy may not always provide the complete picture, leading to the need for more detailed metrics such as precision and recall. Precision measures the percentage of true positive instances out of all instances labeled as positive, while recall assesses the percentage of true positive instances among all actual positive instances. These metrics offer different insights into model performance, especially in binary classifications. However, they can also be applied to other classification tasks beyond binary cases. Understanding and utilizing these metrics helps in providing a nuanced view of model effectiveness and guides necessary improvements.