Scikit-Learn Metrics Tutorials

Welcome to the Scikit-Learn Metrics tutorials section! Here, you will find comprehensive guides on various metrics used in machine learning to evaluate model performance. Whether you are a beginner or an experienced data scientist, these tutorials will help you understand and implement metrics effectively.

Common Metrics in Scikit-Learn

• Accuracy: This metric calculates the proportion of correctly classified observations.
• Precision: It measures the accuracy of positive predictions.
• Recall: Also known as sensitivity, it represents the proportion of actual positives that were correctly identified.
• F1 Score: The harmonic mean of precision and recall, providing a balance between the two.
• ROC-AUC: This metric is used for binary classification problems and measures the model's ability to distinguish between positive and negative classes.

Getting Started

To begin, you can refer to the following tutorials:

• Scikit-Learn Accuracy Tutorial
• Scikit-Learn Precision Tutorial
• Scikit-Learn Recall Tutorial
• Scikit-Learn F1 Score Tutorial
• Scikit-Learn ROC-AUC Tutorial

Advanced Metrics

For more advanced metrics, you can explore the following topics:

• AUC-ROC for Multi-Class Classification
• Balanced Accuracy
• Hamming Loss

Image Recognition Metrics

If you are working on image recognition tasks, you might find the following tutorials helpful:

• Image Recognition Metrics
• Intersection over Union (IoU)

Conclusion

Understanding metrics is crucial for evaluating and improving your machine learning models. These tutorials will provide you with a solid foundation to start using various metrics effectively. Happy learning!

Scikit-Learn Logo