Train and evaluate scikit-learn classification and regression models on structured data.
npx skills add https://github.com/CRAG666/dotfiles --skill scikit-learn-crag666
Please help me install this Agent Skill. Skill: scikit-learn Source: https://github.com/CRAG666/dotfiles/tree/main/skills/scikit-learn Command: npx skills add https://github.com/CRAG666/dotfiles --skill scikit-learn-crag666
scikit-learn provides ready-to-use tools to train, evaluate, and improve machine learning models so you can go from raw data to validated predictions without reinventing core algorithms.
Use the scikit-learn references to build a pipeline that preprocesses numeric and categorical columns, trains a classifier, and reports test metrics for a supervised learning dataset.
High-intent search queries and answers about installing and using this skill.
To train machine learning models without data leakage, use deterministic pipelines that bundle supervised preprocessing and model training together. This ensures transformations like scaling or encoding fit only on training folds during cross-validation, keeping validation data isolated.
The best way to tune hyperparameters for classification models is using grid or random search integrated with cross-validation. This approach systematically searches configurations while evaluating generalization on training folds to reduce overfitting and improve model performance.
Preprocess mixed numeric and categorical features for regression pipelines by applying dedicated preprocessing transformers within a unified workflow. This handles scaling for numeric columns and encoding for categorical columns simultaneously before model training.
Yes, you can use scikit-learn for unsupervised clustering and dimensionality reduction. It supports algorithms like K-Means and DBSCAN for clustering, alongside PCA and t-SNE for reducing feature dimensions on structured datasets.
Yes, the workflow supports standard evaluation metrics for machine learning models. It applies cross-validation and configurable search methods to calculate metrics for both classification and regression, ensuring reproducible model selection on tabular datasets.
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