IFRI-AI-Classes/ifri_mini_ml_lib
Apr 2025 – Apr 2025
1 Month active
Languages Used
NumPyPython
Technical Skills
Algorithm ImplementationClassificationClassification AlgorithmsData ScienceDecision TreesFile Management
Audric LOKO
PROFILE
Audric Loko
Developed a cohesive supervised learning toolkit within the IFRI-AI-Classes/ifri_mini_ml_lib repository, focusing on unifying core classification algorithms and standardizing evaluation metrics. Consolidated KNN, Logistic Regression, and Decision Tree implementations into a single library with consistent fit and predict APIs, leveraging Python and NumPy for efficient algorithm development. Introduced a dedicated module for classification evaluation, supporting confusion matrix, accuracy, precision, recall, and F1 metrics, while refining file structure and documentation for maintainability. This work streamlined onboarding and experimentation, enabling reliable model assessment and faster iteration for downstream users, and addressed both feature development and bug fixes in a production-friendly manner.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
9Total
Bugs
1
Commits
9
Features
1
Lines of code
1,500
Activity Months1
Your Network
11 peopleShared Repositories
11
Work History
April 2025
9 Commits • 1 Features
Apr 1, 2025April 2025: Delivered a cohesive, production-friendly supervised learning toolkit in IFRI-AI-Classes/ifri_mini_ml_lib. Key improvements include a unified Classification Algorithms Library with KNN, Logistic Regression, and Decision Tree, featuring basic fit and predict APIs and refactors to consolidate implementations, plus a dedicated Classification Evaluation Metrics module for standardized model assessment (confusion matrix, accuracy, precision, recall, F1) with name/file cleanup for consistency. Both components were accompanied by documentation updates and naming refinements to improve maintainability and downstream usability.
9 Commits • 1 Features
Apr 1, 2025April 2025: Delivered a cohesive, production-friendly supervised learning toolkit in IFRI-AI-Classes/ifri_mini_ml_lib. Key improvements include a unified Classification Algorithms Library with KNN, Logistic Regression, and Decision Tree, featuring basic fit and predict APIs and refactors to consolidate implementations, plus a dedicated Classification Evaluation Metrics module for standardized model assessment (confusion matrix, accuracy, precision, recall, F1) with name/file cleanup for consistency. Both components were accompanied by documentation updates and naming refinements to improve maintainability and downstream usability.
April 2025
Activity
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Quality Metrics
Correctness89.0%
Maintainability86.6%
Architecture86.6%
Performance77.8%
AI Usage22.2%
Skills & Technologies
Programming Languages
NumPyPython
Technical Skills
Algorithm ImplementationClassificationClassification AlgorithmsData ScienceDecision TreesFile ManagementMachine LearningPythonPython Development
Repositories Contributed To
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