Evaluation Metrics in Machine Learning: A Deep Dive

A blog series of evaluation metrics with real-world use cases.

Image By Paritosh Mahto

Evaluation metrics are an essential tool for assessing the performance of machine learning models. They provide a quantitative measure of the model’s accuracy and reliability and are used to compare the performance of different models, identify areas for improvement, and understand the real-world performance of the model.

In this article, we will explore the different types of evaluation metrics that are commonly used in machine learning, and discuss how to choose the right metric for a given problem.

1. Importance of Evaluation Metrics

Lets start with a basic question. Since we also have loss functions why we need evaluation metrics in Machine learning?

Few Loss Functions of Frequently Used Algorithm

Loss functions and evaluation metrics are similar in that they both measure the performance of a machine learning model. However, there are some key differences between the two:

1. Loss functions are used during training, while evaluation metrics are used to assess the performance of a trained model on unseen data.