A classification report is a performance evaluation metric in machine learning. It is used to show the precision, recall, F1 Score, and support of your trained classification model. If you have never used it before to evaluate the performance of your model then this article is for you. In this article, I will take you through an introduction to the classification report in machine learning and its implementation using Python.
It is one of the performance evaluation metrics of a classification-based machine learning model. It displays your model’s precision, recall, F1 score and support. It provides a better understanding of the overall performance of our trained model. To understand the classification report of a machine learning model, you need to know all of the metrics displayed in the report. For a clear understanding, I have explained all of the metrics below so that you can easily understand the classification report of your machine learning model:
|Precision||Precision is defined as the ratio of true positives to the sum of true and false positives.|
|Recall||Recall is defined as the ratio of true positives to the sum of true positives and false negatives.|
|F1 Score||The F1 is the weighted harmonic mean of precision and recall. The closer the value of the F1 score is to 1.0, the better the expected performance of the model is.|
|Support||Support is the number of actual occurrences of the class in the dataset. It doesn’t vary between models, it just diagnoses the performance evaluation process.|
Hope you now understand what a classification report is in machine learning. Now in the section below, I will walk you through its implementation using Python.
Classification Report using Python
To view the classification report of a machine learning model, we must first train a machine learning model. In the code below, I first trained a very simple machine learning model to classify spam messages and to evaluate its performance I have used a classification report using Python:
precision recall f1-score support ham 0.99 0.99 0.99 1587 spam 0.93 0.92 0.92 252 accuracy 0.98 1839 macro avg 0.96 0.95 0.96 1839 weighted avg 0.98 0.98 0.98 1839
So this is how you can display the classification report of your machine learning model. It is a performance evaluation metric in machine learning which is used to show the precision, recall, F1 Score, and support score of your trained classification model. I hope you liked this article on what is a classification report in machine learning and its implementation using Python. Feel free to ask your valuable questions in the comments section below.