GSoC'24 Progress: Implementing 'margin' function for ClassificationKNN class

Hello everyone!
I'm thrilled to share the updates on the project. Let me walk you through what I've been up to:
  • Achievements
1. Completion of the Loss Function Implementation: I successfully finalized the implementation of the 'loss' function for the ClassificationKNN model. This function is pivotal as it measures the error between predicted and actual class labels, steering the model toward improved accuracy. My mentor, Andreas, fixed the bug in the 'Prior' property, which initially hindered the normalization of observation weights; the 'loss' function now performs as expected.
2. Implementation of the Margin function: Besides the 'loss' function, I have implemented the 'margin' function. The function calculates the difference between the predicted probability for the true class and the highest predicted probability for the false classes.
  • Current Focus
Implementing the PartialDependence function: My current task involves implementing the 'partialDependence' function for the ClassificationKNN model. This function is essential for understanding the relationship between feature values and predicted outcomes.
Stay tuned for more exciting developments!
Thank You! 😀

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