Use when preparing data for machine learning from SQL Server — feature engineering in T-SQL, building training/test datasets, statistical aggregations for ML pipelines, sampling strategies, data normalization and encoding in SQL, writing queries that feed pandas or scikit-learn, exporting to Parquet or CSV for model training, or when a data scientist asks for a 'feature table' or 'training set' from a SQL Server database.
100
100%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
{
"name": "alonso-skills/sql-ml-features",
"version": "0.1.0",
"private": false,
"summary": "Use when preparing data for machine learning from SQL Server — feature engineering in T-SQL, building training/test datasets, statistical aggregations for ML pipelines, sampling strategies, data normalization and encoding in SQL, writing queries that feed pandas or scikit-learn, exporting to Parquet or CSV for model training, or when a data scientist asks for a 'feature table' or 'training set' from a SQL Server database.",
"skills": {
"sql-ml-features": {
"path": "SKILL.md"
}
}
}