Metrics to Evaluate your Machine Learning Algorithm: Accuracy, Precision, Recall, Specificity, and F1.

This article will discuss the most common ML model evaluation metrics such as Accuracy, Precision, Recall, Specificity, and F1 Score for a classification problem in the fintech space.

This article is part of a series where we walk step by step through solving fintech problems with different Machine Learning techniques using the “All lending club loan” dataset. Here you can find the complete end-to-end data science project for beginners to learn data science.

Introduction to Data Science with python: A complete guide to learn data science and machine…

We have walked through the confusion matrix in the previous article, and I suggest you start from there.

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If you are preparing for an interview, this article would help you to answer the following questions:

1. What are precision and recall?
2. What error metric would you use to evaluate how good a binary classifier is?