Hyperparameter-Optimization-for-Decision-Trees-Using-Optuna rilufiyy · COMPLETED
This repository showcases Decision Tree hyperparameter optimization using Optuna. It automates tuning parameters like max_depth, min_samples_split, min_samples_leaf, and criterion to improve model performance. Easily adaptable for classification or regression tasks, offering an efficient alternative to Grid or Random Search.
github.com/rilufiyy/Hyperparameter-Optimization-for-Decision-Trees-Using-Optuna · ★ 1 · Forks 0 · Size 220 KB
SUMMARY
Technologies 4
Scored 4
Observed 4
Practices 6
Evidence 4
Skips 0
COVERAGE
Analyzed 5 files · 3 commits · 0 API calls
TECHNOLOGIES & DEPTH
Markdown LANGUAGE Depth 70
1 files · PRODUCTION
Python LANGUAGE Depth 70
1 files · PRODUCTION
NumPy LIBRARY Depth 54
1 files · PRODUCTION
pandas LIBRARY Depth 54
1 files · PRODUCTION
PRACTICES
documentation · observedautomated_tests · absentcontinuous_integration · absentcontainerization · absentlinting · absentformatting · absent
ACTIVITY & OWNERSHIP
First commit 2025-09-05
Last commit 2025-09-08
Active months 1
Commits 3