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scientific-ml-regression

Train and compare scikit-learn regression models on multi-target datasets.

3|1|Updated Feb 11, 2026
Search Tags:#regression#cross-validation#machine-learning#scikit-learn#model-comparison#multi-target#openml
One-click install
npx skills add https://github.com/nahisaho/satori --skill scientific-ml-regression
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-ml-regression
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-ml-regression
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-ml-regression

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about scientific-ml-regression

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compare multiple regression models for a multi-target dataset?▼

Multi-target regression is handled by training and evaluating multiple scikit-learn models like Ridge, Lasso, RandomForest, GradientBoosting, and ExtraTrees, comparing their cross-validated performance metrics to select the best model.

How do I use cross-validation to evaluate scikit-learn regression models?▼

Cross-validation evaluates scikit-learn regression models by applying KFold to estimate generalization performance across multiple targets, ensuring consistent benchmarking for Ridge, Lasso, and tree-based models.

Can I retrieve datasets from OpenML for multi-target regression benchmarking?▼

Yes, the pipeline includes OpenML integration to fetch datasets directly for multi-target regression benchmarking, allowing immediate training and comparison of scikit-learn models.

What is the best way to automate scikit-learn model comparison on OpenML datasets?▼

The best way is using a unified pipeline that handles end-to-end training, KFold cross-validation, and performance evaluation for multiple targets to identify the top performing algorithm on OpenML datasets.

Does multi-target regression with scikit-learn support GradientBoosting and ExtraTrees?▼

Yes, the unified comparison pipeline explicitly trains and evaluates GradientBoosting and ExtraTrees alongside Ridge, Lasso, and RandomForest for multi-target regression.

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