machine-learning-foundations
Supervised and unsupervised learning, bias-variance tradeoff, cross-validation, decision trees, ensemble methods, neural network fundamentals, and the practitioner's workflow from problem framing through deployment. Covers classification, regression, clustering, dimensionality reduction, regularization, hyperparameter tuning, and evaluation metrics. Use when building predictive models, selecting algorithms, or understanding the machine learning pipeline.
npx skills add majiayu000/claude-skill-registry --skill machine-learning-foundations --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.
# Machine Learning Foundations Machine learning is the practice of building systems that learn patterns from data and use those patterns to make predictions or decisions on new data. Where statistical modeling (the inference culture) asks "what is the relationship between X and Y?", machine learning (the prediction culture) asks "given X, what is the best prediction of Y?" This skill covers the foundational concepts, algorithms, and workflow of machine learning from the practitioner's perspective. **Agent affinity:** breiman (algorithm selection, ensemble methods), tukey (feature engineering, EDA) **Concept IDs:** data-correlation, data-distributions, data-measures-of-spread, data-hypothesis-testing ## The ML Workflow | Stage | Goal | Key operations | |---|---|---| | 1. Problem framing | Define the task precisely | Classification vs. regression vs. clustering; define target variable and success metric | | 2. Data collection | Assemble training data | Sources, sampling, labeling; ensure data represents the deployment population | | 3. Feature engineering | Create informative inputs | Domain-driven features, transformations, encoding categoricals | | 4. Train/test split | Prevent ove
- The ML Workflow
- Supervised Learning
- Classification
- Regression
- Evaluation Metrics
- The Bias-Variance Tradeoff
- Cross-Validation
- k-Fold Cross-Validation
- Critical Rules
- Decision Trees
- How They Work
- Why Single Trees Overfit
- Ensemble Methods
- Bagging (Bootstrap Aggregating)
What does the machine-learning-foundations skill do?
Supervised and unsupervised learning, bias-variance tradeoff, cross-validation, decision trees, ensemble methods, neural network fundamentals, and the practitioner's workflow from problem framing through deployment. Covers classification, regression, clustering, dimensionality reduction, regularization, hyperparameter tuning, and evaluation metrics. Use when building predictive models, selecting algorithms, or understanding the machine learning pipeline.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill machine-learning-foundations --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 majiayu000/claude-skill-registry, a repository with 534 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.