How to create multi-label confusion matrix
by vigneshchennai74 Updated: Apr 6, 2023
A multi-label confusion matrix is a useful tool for evaluating the performance of multi-label classification models. It provides a detailed view of the true positive (TP), false positive (FP), false negative (FN), and true negative (TN) predictions made by the classifier for each label. This information can be used to evaluate several aspects of the classifier's performance, including:
- Accuracy: The overall accuracy of the classifier can be computed as the ratio of correct predictions to total predictions.
- Precision: Precision measures the fraction of correct positive predictions. It can be used to evaluate the quality of the positive predictions made by the classifier.
- Recall: Recall measures the fraction of actual positive instances correctly identified by the classifier. It can be used to evaluate the completeness of the positive predictions made by the classifier.
- F1-Score: The F1-score is the harmonic mean of precision and recall and provides a balance between precision and recall.
- Support: The support is the number of instances belonging to each class.
These performance metrics can be computed for each label and averaged across labels to give an overall view of the classifier's performance.
In addition to these performance metrics, the multi-label confusion matrix can also help identify specific areas for improvement in the classifier. For example, if the classifier has low precision for a particular label, it may indicate that it is making too many false positive predictions. On the other hand, if the classifier has a low recall for a particular label, it may indicate that the classifier needs to include more actual positive instances for that label. By identifying these specific areas for improvement, the multi-label confusion matrix can help guide further development and refinement of the classifier.
Preview of the output that you will get on running this code from your IDE
In this solution we have used Sklearn library.
- Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
- Run the file to create multi label confusion matrix.
I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.
I found this code snippet by searching for "Multi-label confusion matrix" in kandi. You can try any such use case!
scikit-learn: machine learning in Python
Python 54472 Version:1.2.2 License: Permissive (BSD-3-Clause)
If you do not have Scikit-learn that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the Scikit-learn page in kandi.
You can search for any dependent library on kandi like Scikit-learn.
I tested this solution in the following versions. Be mindful of changes when working with other versions.
- The solution is created in Python 3.7.15 version
- The solution is tested on scikit-learn 1.0.2 version
- The solution is tested on numpy 1.21.6 version
Using this solution, we are able to create a multi-label confusion matrix using Scikit learn library in Python with simple steps. This process also facilities an easy-to-use, hassle-free method to create a hands-on working version of code which would help us label for confusion matrix in Python.
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