F-beta is the weighted harmonic mean of the precision and recall. It is used as a performance evaluation measure for classification-based machine learning models. If you’ve never used this performance measurement metric before to evaluate your classification models, this article is for you. This article will introduce you to the F-beta score in machine learning and its implementation using Python.
F-Beta Score
F-beta is the weighted harmonic mean between precision and recall. When the F-beta score of your machine learning model is closer to 1, it means that the model is trained well, and if it is closer to 0, it means that the model is not well trained. When using it to evaluate the performance of a machine learning model, you should know that:
- if you set the beta value greater than 1, it will give more importance to the recall when evaluating the performance of your model;
- and if you set the beta value less than 1, it will give more importance to the precision;
Thus, the beta value equal to 1 results in a perfect balance between precision and recall. Hope you now have understood what the F-beta score in machine learning is. Now in the section below, I will walk you through its implementation using Python.
F-Beta Score using Python
To calculate the F-beta score of a machine learning model using Python, I will first train a classification model and then calculate its F-beta as shown in the code below:
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Summary
So this is how you can evaluate the performance of your machine learning model by using F-beta. It is the weighted harmonic mean between precision and recall. When the F-beta score of your machine learning model is closer to 1, it means that the model is trained well and if it is closer to 0, it means that the model is not well trained. I hope you liked this article on an introduction to F-beta in machine learning and its implementation using Python.
