Introduction
In this project, you will learn how to implement a confusion matrix, which is a fundamental tool for evaluating the performance of a classification model. The confusion matrix provides a detailed breakdown of the model's predictions, allowing you to identify areas for improvement and gain valuable insights into the model's strengths and weaknesses.
🎯 Tasks
In this project, you will learn:
- How to implement the
confusion_matrix
function to compute the confusion matrix for a classification problem - How to test and refine the
confusion_matrix
function to handle edge cases and improve its robustness - How to document the
confusion_matrix
function to make it more user-friendly and easier to understand - How to integrate the
confusion_matrix
function into a larger machine learning project and use it to evaluate the performance of a classification model
🏆 Achievements
After completing this project, you will be able to:
- Compute and interpret the confusion matrix for a classification problem
- Apply techniques for handling edge cases and improving the robustness of a function
- Implement best practices for documenting and making code more user-friendly
- Apply the confusion matrix in the context of a larger machine learning project