scikit-learn-machine-learning
Classical ML in Python: classification, regression, clustering, dim reduction, evaluation, tuning, preprocessing pipelines. Linear models, tree ensembles, SVMs, K-Means, PCA, t-SNE. Use PyTorch/TF for deep learning; XGBoost/LightGBM for scale.
npx skills add BioTender-max/awesome-bio-agent-skills --skill scikit-learn-machine-learning --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# scikit-learn ## Overview scikit-learn is the standard Python library for classical machine learning. It provides consistent APIs for supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, and preprocessing, with seamless integration into NumPy/pandas workflows. ## When to Use - Building classification models for labeled data (spam detection, disease diagnosis, species identification) - Predicting continuous outcomes with regression (price prediction, dose-response modeling) - Clustering unlabeled data into groups (patient stratification, gene expression clusters) - Reducing dimensionality for visualization or feature engineering (PCA, t-SNE on multi-omics data) - Evaluating and comparing model performance with cross-validation - Tuning hyperparameters systematically (grid search, random search) - Building reproducible ML pipelines with preprocessing and modeling steps - For deep learning tasks (images, NLP), use `pytorch` or `transformers` instead - For large-scale gradient boosting, use `xgboost` or `lightgbm` instead ## Prerequisites - **Python packages**: `scikit-learn`, `numpy`, `pandas` - **Optional**
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Module 1: Data Preprocessing
- Module 2: Supervised Learning — Classification
- Module 3: Supervised Learning — Regression
- Module 4: Unsupervised Learning — Clustering
- Module 5: Dimensionality Reduction
- Module 6: Model Evaluation & Selection
- Module 7: Pipelines
- Common Workflows
- Workflow 1: End-to-End Classification
pip install scikit-learn numpy pandas matplotlib seaborn
What does the scikit-learn-machine-learning skill do?
Classical ML in Python: classification, regression, clustering, dim reduction, evaluation, tuning, preprocessing pipelines. Linear models, tree ensembles, SVMs, K-Means, PCA, t-SNE. Use PyTorch/TF for deep learning; XGBoost/LightGBM for scale.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill scikit-learn-machine-learning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 144 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.