scikit-learn — classical machine learning reference: classification, regression, pipelines, tuning
The scikit-learn field manual — estimator API, preprocessing and ColumnTransformer, pipelines, classification and regression, cross-validation, hyperparameter tuning, anti-patterns
- What
- The scikit-learn field manual — estimator API, preprocessing and ColumnTransformer, pipelines, classification and regression, cross-validation, hyperparameter tuning, anti-patterns
- Cost
- Free
- Needs
- a Python environment with scikit-learn installed (pip install scikit-learn); tabular data to work with
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — @tondevrel's scikit-learn skill: a dense field manual for classical machine learning with scikit-learn. The agent gets the estimator interface contract (fit/transform/predict), when to use and when NOT to use sklearn (tabular data yes; deep learning, large-scale NLP, big data, and real-time streaming go elsewhere), the canonical train/predict pattern, critical rules (split before anything, always pipelines, scale data, stratify, cross-validate, impute, encode), anti-patterns with before/after code (data leakage from fitting scalers on the whole dataset, manual preprocessing instead of pipelines), preprocessing recipes (scaling, encoding, ColumnTransformer), classification and regression algorithm references, evaluation metrics, GridSearchCV/RandomizedSearchCV tuning, PCA, K-Means/DBSCAN clustering, end-to-end workflow examples (including a custom TransformerMixin feature-engineering class), and performance notes (n_jobs, partial_fit for online learning). Honest caveats: a reference, not a course — assumes you already know what classification vs regression means; examples are illustrative snippets, not a tested codebase. License: the manifest records MIT (the manifest makes faith); the repo frontmatter claims BSD-3-Clause — divergence noted, MIT retained. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: a Python environment with scikit-learn installed (pip install scikit-learn); tabular data to work with Install "scikit-learn — classical machine learning reference: classification, regression, pipelines, tuning" for me. It gives my agent @tondevrel's scikit-learn field manual: the estimator API contract, when to use (and not use) sklearn, the canonical train/predict pattern, critical rules (split first, pipelines always, scale, stratify, cross-validate), anti-patterns with before/after fixes (data leakage, manual preprocessing), preprocessing recipes (scaling, encoding, ColumnTransformer), classification/regression algorithm references, evaluation metrics, GridSearchCV/RandomizedSearchCV tuning, PCA, clustering, end-to-end workflow examples, and performance notes. MIT licensed (per the discovery manifest; the repo frontmatter claims BSD-3-Clause — divergence noted). Repository: https://github.com/tondevrel/scientific-agent-skills/blob/main/skills/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 "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. install scikit-learn, prepare my tabular dataset, then describe the ML task to the agent). 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.