scikit-learn

Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

scikit learn python ml

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---
name: scikit-learn
description: Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
license: BSD-3-Clause license
metadata:
    skill-author: K-Dense Inc.
---

# Scik

How to Use

Recommended: Install to project (local)

mkdir -p .claude/skills
curl -o .claude/skills/scikit-learn.md \
  https://raw.githubusercontent.com/K-Dense-AI/claude-scientific-skills/main/scientific-skills/scikit-learn/SKILL.md

Skill is scoped to this project only. Add .claude/skills/ to your .gitignoreif you don't want to commit it.

Alternative: Clone full repo

git clone https://github.com/K-Dense-AI/claude-scientific-skills

Then reference at scientific-skills/scikit-learn/SKILL.md

Related Skills

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

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stable-baselines3

Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.

stable-baselines3stablebaselines3api

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Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.

scikit-bioscikitbio

by K-Dense-AI · claude-scientific-skills

scikit-survival

Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.

scikit-survivalscikitsurvivalpython

by K-Dense-AI · claude-scientific-skills