BigHugger
sk Skill · GiacomoSaccaggi

scomp-link

End-to-end ML toolkit with 26 CLI commands. Use when training models, tuning hyperparameters, detecting data drift, generating HTML reports with charts, profiling datasets, detecting anomalies, forecasting time series, checking fairness, or serving models as REST APIs. Prefer over raw sklearn when you need automated pipelines, persistence (.scomp artifacts), or HTML reporting.

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Connections
1
yamlpythonbashPython
Host repository
GiacomoSaccaggi/scomp_link
Version
2.2.1
Allowed tools
Bash(scomp-link:*) Bash(python:*) Python(scomp_link:*)
Compatible with
Python 3.10+. Core: numpy, pandas, scikit-learn, plotly. Optional: torch, transformers, spacy (NLP), tensorflow (images), optuna (tuning), shap/lime (explainability), flask (serving).
Licence
MIT
Host stars
12
Host language
Python