Understanding machine learning model evaluation metrics — Part 1

Navigating the Metrics Landscape: A Comprehensive Guide to Evaluating Classification Models in Machine Learning

Machine learning is the process of creating and applying algorithms that can learn from data and make predictions or decisions. Machine learning models are often evaluated based on how well they perform on a given task, such as classification, regression, clustering, etc. To measure the performance of a machine learning model, we need to use some metrics that can quantify the quality and accuracy of the model’s predictions or outputs. In this article, we will focus on four common metrics for classification problems: accuracy, precision, recall, and F1 score. We will see what these metrics mean, how to calculate them, and how to implement them using Python libraries such as NumPy, Pandas, and Scikit-learn.

Accuracy

Accuracy is one of the simplest and most intuitive metrics for evaluating classification models. It is defined as the ratio of correct predictions to the total number of predictions. In other words, accuracy tells us how often the model predicts the correct class for a given input. The formula for accuracy is:

Accuracy is a good metric to use when the classes are balanced, meaning that there are roughly equal numbers of positive and negative examples in the dataset. However, accuracy can be misleading when the classes are…