skills.rest
nahisaho
nahisahoCOMMUNITY
Explore all skills by this author →
GitHub

scientific-ml-classification

Benchmark multiple classification models with StratifiedKFold cross-validation and report ROC-AUC metrics.

3|1|Updated Feb 11, 2026
Search Tags:#classification#cross-validation#ml#model-evaluation#confusion-matrix#stratifiedkfold#roc_auc
One-click install
npx skills add https://github.com/nahisaho/satori --skill scientific-ml-classification
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-ml-classification
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-ml-classification
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-ml-classification

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Standardizes evaluation of multiple classification models using StratifiedKFold cross-validation to ensure fair comparisons and robust metrics.

Core Features & Use Cases

  • Supports multiple classifiers (Logistic Regression, Random Forest, Gradient Boosting, SVM, XGBoost) and evaluates them with ROC-AUC, accuracy, precision, recall, F1, and confusion matrices.
  • Integrates OpenML data retrieval for benchmark datasets and reproducible experiments.
  • Provides a structured workflow from model definition to metrics reporting and result visualization.

Quick Start

Run a cross-validated comparison of multiple classifiers on a dataset and output ROC-AUC and confusion matrices.

Frequently Asked Questions about scientific-ml-classification

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

FAQPage Schema
How do I compare multiple classification models using cross-validation?▼

Compare multiple classification models using StratifiedKFold cross-validation to ensure fair evaluations across datasets. It benchmarks classifiers like Logistic Regression and Random Forest, reporting ROC-AUC, accuracy, precision, recall, and F1 metrics.

What is the best way to evaluate binary and multi-class classifiers fairly?▼

Evaluating binary and multi-class classifiers fairly requires StratifiedKFold cross-validation to maintain class distribution across folds. This approach generates robust metrics like ROC-AUC, precision, recall, and confusion matrices to guide model selection.

How do I benchmark classifiers on OpenML datasets with scikit-learn?▼

Benchmark classifiers on OpenML datasets by integrating OpenML data retrieval within a scikit-learn workflow. Define models, execute StratifiedKFold cross-validation, and produce structured results including ROC-AUC scores and confusion matrices for reproducible experiments.

Does this cross-validation approach support XGBoost and SVM models?▼

Yes, the cross-validation approach supports XGBoost and SVM models alongside Logistic Regression, Random Forest, and Gradient Boosting. It evaluates all supported classifiers uniformly using StratifiedKFold to produce comparable ROC-AUC and confusion matrix results.

Why use StratifiedKFold instead of standard k-fold cross-validation?▼

Use StratifiedKFold instead of standard k-fold cross-validation to ensure class proportions are preserved across all folds. This stratification prevents skewed evaluations on imbalanced datasets, providing reliable ROC-AUC, precision, and recall metrics for model selection.

Related Skills

Explore More Data & Analytics Skills→
datamonsterrdatamonsterr

scikit-learn

Build and evaluate classical machine learning models with scikit-learn pipelines.

Community
Advanced
rocklambrosrocklambros
2

evaluating-binary-classifiers

Evaluate binary classifiers with ROC-AUC, PR-AUC, calibration, and bootstrap confidence intervals.

Community
Advanced
nahisahonahisaho
3

scientific-ml-regression

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

Community
Advanced
GuitarAlchemistGuitarAlchemist

ix-supervised

Train and evaluate supervised machine learning models in Rust.

Official
Advanced