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

I am excited to share the latest progress on my project. Since my previous blog post, I have been working diligently on implementing a 'loss' function for the 'ClassificationKNN' model. This journey has been challenging and rewarding, pushing me to overcome several obstacles.

Achievements

  1. Implementation of the 'loss' Function: I successfully implemented the 'loss' function for the 'ClassificationKNN' model. This function is crucial as it measures error between the predicted and actual class labels, guiding the model towards better performance. 
  2. Overcoming Challenges: One of the main difficulties I encountered was normalizing the weights of observations. I discovered a bug in the 'Prior' property of the 'ClassificationKNN' object, which was supposed to help normalize the weights. This bug is a significant roadblock. 
Next Steps

  1. Fixing the Bug: I will delve into the 'ClassificationKNN' function to locate and correct the error. This step is crucial to ensure that the weights are normalized correctly, which in turn will improve the performance of the 'loss' function.
  2. Testing the Loss Function: Once the bug is fixed, I will rigorously test the 'loss' function to verify its effectiveness.

Implementing the 'loss' function and dealing with the unexpected bug has been a learning experience. It highlighted importance of thorough testing and debugging in software development. 

I am optimistic about the next steps and excited to see the improvements in the 'ClassificationKNN' class. I look forward to sharing more updates with you as I refine and enhance this project.

Thank You! 😃

Comments

Post a Comment

Popular posts from this blog

GSoC'24 Project: Adding GAM and Discriminant Classification Classes and Implementing Missing Methods

I am Ruchika Sonagote from India, and I am currently studying Computer Science at the Indian Institute of Technology Gandhinagar. I am thrilled to share that I will contribute to the GNU Octave Statistics Package this summer. I am thankful to Andreas Bertsatos for mentoring me throughout this project. I am enthusiastic about the opportunity to contribute to the GNU Octave Statistics Package and am committed to making meaningful contributions to its development. This project aims to significantly extend the classification capabilities of the GNU Octave statistics package by implementing additional methods for the existing `ClassificationKNN` class and introducing new classification classes, namely `ClassificationDiscriminant` and `ClassificationGAM`. These enhancements will bridge the gap between GNU Octave and MATLAB in terms of advanced data analysis and machine learning functionalities, fostering a richer environment for scientific computing. Proposed Timeline ...
Read more

GSoC'24 Week 1 & 2 Progress: Building a Cross-Validation Framework in Octave

Image
In the past two weeks, I have been working on enhancing the statistics package for GNU Octave by implementing a cross-validation framework for classification models. The focus has been on implementing a 'crossval' method in the 'ClassificationKNN' class and creating a ‘ClassificationPartitionedModel’ class and a ‘kfoldPredict’ method. Together, these provide a solid foundation for cross-validation in Octave. This blog will walk you through the progress, challenges, and solutions implemented.  Cross-validation is a method used to evaluate and improve the performance of machine learning models. It is essential in classification models, where the goal is to categorize data into predefined classes. Cross-validation helps assess how well a model will generalize to an independent dataset, which is critical for ensuring its robustness and reliability. The process involves partitioning the dataset into a set of folds, training the model on some folds, and then validating it on ...
Read more