scientific-ml-regression
CommunityUnified multi-target regression model comparison.
Data & Analytics#regression#cross-validation#machine-learning#scikit-learn#model-comparison#multi-target#openml
Authornahisaho
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Automates the end-to-end workflow of training, evaluating, and comparing multiple regression models on multi-target data, enabling consistent benchmarking across experiments.
Core Features & Use Cases
- Unified pipeline for training, evaluating, and comparing Ridge, Lasso, Random Forest, Gradient Boosting, and Extra Trees across multiple targets.
- Supports multi-target regression and cross-validation (KFold) to estimate generalization performance.
- OpenML integration for dataset retrieval and benchmarking.
Quick Start
Run the unified regression pipeline on your multi-target dataset to train and compare the listed models using cross-validated metrics.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: scientific-ml-regression Download link: https://github.com/nahisaho/satori/archive/main.zip#scientific-ml-regression Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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