.. currentmodule:: scikitplot.feature_selection .. _feature_selection-index: Feature Selection ================= Feature profiling, lineage and drift ---------------------------------------------------------------------- ``scikitplot.feature_selection`` helps with tables that have too many columns to reason about one by one. It answers three questions that are usually answered by hand and forgotten: * **What is each feature?** Role, dtype family, semantic kind, user tags, a bin recommendation, and policy-backed assessments such as *skewed*, *discrete*, *constant*, *binary* or *identifier-like*, each with evidence and an explicit ``unknown`` / ``not_applicable`` state. * **What happened to each feature in a fitted pipeline?** Which steps touched it, which columns it became, where it was dropped, and how a model's importances map back to raw columns. * **Has it drifted?** A frozen training reference compared with later data. .. important:: Profile the **training** split only. Validation, test and production data are audited against the fitted schema and the frozen reference; they are never profiled into them. .. toctree:: :maxdepth: 2 :hidden: getting_started configuration pipelines_and_lineage drift .. grid:: 1 1 2 2 :gutter: 2 .. grid-item-card:: Getting started :link: feature_selection-getting-started :link-type: ref Profile a table, ask features about themselves, group and select them. .. grid-item-card:: Configuration :link: feature_selection-configuration :link-type: ref Policies in TOML, YAML or JSON; thresholds; overrides; custom assessments. .. grid-item-card:: Pipelines and lineage :link: feature_selection-lineage :link-type: ref Route columns in a ColumnTransformer, trace a fitted pipeline, attribute importances. .. grid-item-card:: Drift :link: feature_selection-drift :link-type: ref Freeze a training reference and judge later data feature by feature. Examples -------- See :ref:`feature_selection_examples` for executable examples that accompany this guide.