scikit-learn — classical machine learning playbook (short listing)
Short listing — license not verifiable: algorithm selection tables for classification, regression, clustering, and dimensionality reduction, plus pipelines, cross-validation strategies, evaluation metrics, hyperparameter tuning, and common pitfalls
- What
- Short listing — license not verifiable: algorithm selection tables for classification, regression, clustering, and dimensionality reduction, plus pipelines, cross-validation strategies, evaluation metrics, hyperparameter tuning, and common pitfalls
- Cost
- Free
- Needs
- Python 3 with scikit-learn installed — a classification, regression, or clustering problem to work on
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Selected by Skill Harbor — short listing (the repo frontmatter carries no license field and the discovery manifest records null — the manifest makes faith, so no content is reproduced): @eyadsibai's scikit-learn skill (part of the ltk plugin collection) — a compact, practical playbook for classical machine learning with scikit-learn. It covers algorithm selection tables (Logistic Regression, Random Forest, Gradient Boosting, SVM, KNN for classification; Ridge/Lasso, Random Forest, Gradient Boosting for regression; KMeans, DBSCAN, Agglomerative, Gaussian Mixture for clustering; PCA, t-SNE, UMAP for dimensionality reduction), pipeline concepts (Pipeline, ColumnTransformer, FeatureUnion, leakage prevention), cross-validation strategies (KFold, StratifiedKFold, TimeSeriesSplit, LeaveOneOut), evaluation metrics per task, hyperparameter tuning methods (GridSearchCV, RandomizedSearchCV, HalvingGridSearchCV), and common pitfalls with fixes. Honest caveats: license terms not verifiable — short listing with a link only, nothing copied; scikit-learn version drift matters (e.g. HalvingGridSearchCV needs sklearn 0.24+); follow the source link and read the terms yourself before use. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: Python 3 with scikit-learn installed — a classification, regression, or clustering problem to work on Install "scikit-learn — classical machine learning playbook (short listing)" for me. It gives my agent @eyadsibai's scikit-learn playbook: algorithm selection tables for classification, regression, clustering, and dimensionality reduction, pipeline concepts with leakage prevention, cross-validation strategies, per-task evaluation metrics, hyperparameter tuning methods, best practices, and common pitfalls with fixes. IMPORTANT: the license terms were not verifiable (the discovery manifest records no license) — fetch from the link only, reproduce nothing beyond the link, and read the terms yourself before use. Repository: https://github.com/eyadsibai/ltk/blob/main/plugins/ltk-data/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. pip install scikit-learn; check your sklearn version supports the tuning methods you plan to use; read the license terms before reuse). 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.