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scikit-learn

combined

Supports machine learning in Python with scikit-learn. Applies when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices. Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

When to use

• Building classification or regression models
• Performing clustering or dimensionality reduction
• Preprocessing and transforming data for machine learning
• Evaluating model performance with cross-validation
• Tuning hyperparameters with grid or random search
• Creating ML pipelines for production workflows
• Comparing different algorithms for a task
• Working with both structured (tabular) and text data

When not to use

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Skill page

Conflict

The K-Dense-AI copy is the most complete and current, but the other copies contain material constraints and steps it lacks. The foryourhealth111-pixel copy adds a critical scope boundary (excluding causal identification) and a specific use case (ordinary regression) that the K-Dense copy omits. The sickn33 copy adds a mandatory pre-execution step to read a detailed guide and specific script execution commands. The zLanqing copy is a truncated version of the K-Dense content. Because the K-Dense copy lacks the causal analysis exclusion and the sickn33 copy lacks the detailed algorithmic reference, crowning one would hide real alternatives and constraints.