machine-learning
De la autor
Use when predicting a column from rows of tabular features with classic models — scikit-learn pipelines, RandomForest, XGBoost/LightGBM, leak-free cross-validation, metrics for imbalanced classes, or a model that aced CV then collapsed in production. NOT PyTorch neural nets (that is `deep-learning`), NOT cleaning the dirty table first (that is `data-cleaning`), NOT forecasting a dated series (that is `forecasting`), NOT text/token modeling (that is `nlp`).
Instalare
npx skills add ericrisco/rsc-harness --skill machine-learning -a claude-code
Se instalează în .claude/skills/machine-learning/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
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De ce 85
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
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- deep-learningericrisco/rsc-harnessUse when training or debugging a neural net in PyTorch — the forward/loss/backward/step loop and its silent bugs, mixed precision (AMP), AdamW/LR schedules, DDP/FSDP/ZeRO, checkpoints and seeds. NOT LoRA/QLoRA on a pretrained LLM (that is `finetuning`), NOT tabular sklearn/XGBoost (that is `machine-learning`), NOT tokenization or NLP metrics (that is `nlp`).Inginerie★ 255
- matlab-interpret-machine-learning-modelmatlab/matlab-agentic-toolkitInterpret and explain a trained tabular machine-learning model (classification or regression) in MATLAB. Find which predictors, features, or columns matter most; explain why the model made a specific prediction, including diagnosing predictions it got wrong; show how a predictor affects the output; and compare how the model behaves across cohorts or subgroups. Uses model-agnostic techniques and model-native measures, and works on custom models (such as a dlnetwork) through a prediction function handle. Use for model interpretability, explainability, and feature-importance questions on tabular data, not for training, tuning, feature selection, deploying models, or models trained on image, text, signal, or other non-tabular data.DevOps★ 1.2K
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