scikit-learn — classical machine learning reference
Classical ML with scikit-learn: supervised and unsupervised algorithms, preprocessing, pipelines and composition, cross-validation, hyperparameter tuning, model evaluation, best practices, and worked examples — verified against scikit-learn 1.8-1.9
- What
- Classical ML with scikit-learn: supervised and unsupervised algorithms, preprocessing, pipelines and composition, cross-validation, hyperparameter tuning, model evaluation, best practices, and worked examples — verified against scikit-learn 1.8-1.9
- Cost
- Free
- Needs
- Python 3 with scikit-learn installed (uv pip install scikit-learn; >= 1.8 recommended, verified against 1.8-1.9) — pandas, numpy, matplotlib, seaborn commonly used alongside; your own tabular/text data
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — @alterlab-ieu's alterlab-scikit-learn skill, listed here with credit to its creator (part of the AlterLab Academic Skills suite): a complete agent reference for classical machine learning with scikit-learn. It covers supervised learning (linear models, trees, SVMs, ensembles, Naive Bayes, KNN, MLPs), unsupervised learning (K-Means, DBSCAN/HDBSCAN, agglomerative, GMMs, PCA, t-SNE, UMAP), model evaluation and selection (KFold/StratifiedKFold/TimeSeriesSplit, GridSearchCV/RandomizedSearchCV/HalvingGridSearchCV, per-task metrics), data preprocessing (scalers, encoders, imputers, feature engineering), and pipelines and composition (Pipeline, ColumnTransformer, FeatureUnion) — with two runnable example scripts (classification pipeline, clustering analysis), worked examples, and a best-practices/troubleshooting reference. Refreshingly current: verified against scikit-learn 1.8-1.9, including breaking-change notes (LogisticRegression penalty deprecation, SVC(probability=True) removal path, squared=False removal) and a routing table for when to use sibling skills (PyMC for Bayesian, scikit-survival for censored outcomes, SHAP for explanations, statsmodels for inference). Honest caveats: scikit-learn is CPU classical ML — for deep learning or GPUs, look elsewhere; version drift is real (the 1.8-1.9 notes help); your data comes from you — the skill is patterns and code, not data. MIT licensed. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: Python 3 with scikit-learn installed (uv pip install scikit-learn; >= 1.8 recommended, verified against 1.8-1.9) — pandas, numpy, matplotlib, seaborn commonly used alongside; your own tabular/text data Install "scikit-learn — classical machine learning reference" for me. It gives my agent @alterlab-ieu's scikit-learn reference: supervised and unsupervised algorithms, preprocessing (scalers, encoders, imputers, feature engineering), pipelines and composition (Pipeline, ColumnTransformer, FeatureUnion), cross-validation and hyperparameter tuning, per-task metrics, two runnable example scripts (classification_pipeline.py, clustering_analysis.py), worked examples, and best-practices/troubleshooting — with 1.8-1.9 version notes and a routing table for sibling skills (PyMC, scikit-survival, UMAP, SHAP, statsmodels). Part of the AlterLab Academic Skills suite. MIT licensed. Repository: https://github.com/alterlab-ieu/alterlab-academic-skills/blob/main/skills/data-science/alterlab-scikit-learn/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "alterlab-scikit-learn". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. uv pip install scikit-learn (plus pandas, numpy, matplotlib, seaborn as needed); check my sklearn version against the 1.8-1.9 notes; always preprocess inside a Pipeline, fit on train only, stratify classification splits). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.