Model evaluation is a critical step in machine learning. It involves assessing the performance of trained models to determine their effectiveness in making predictions or classifications. This evaluation process helps us understand how well our models generalize to unseen data and guides us in making informed decisions.

When evaluating models, it is essential to select appropriate metrics that align with our specific goals and factors of interest. For example, if we are dealing with a classification problem, accuracy, precision, recall, and F1 score are commonly used metrics. On the other hand, regression problems often rely on metrics like mean squared error (MSE), root mean squared error (RMSE), and R-squared.

Throughout this series, we will delve into various evaluation metrics, explore their strengths and limitations, and provide practical examples of how to use and interpret them in real-world scenarios.