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defects/sklearn/patch/sklearn-0001-gradient-boosting-feature-names-index.md

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+# sklearn-0001: HistGradientBoosting _check_categories — O(n²) feature_names.index in loop
+
+**Severity:** MEDIUM
+**CWE:** CWE-407 (Algorithmic Complexity — linear membership test in hot loop)
+**Speedup:** >40x at F=1000 features, C=200 categorical features
+**Target:** scikit-learn (scikit-learn/scikit-learn)
+**File:** `sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:440-443`
+
+## Description
+
+`_check_categories` resolves categorical feature names to integer indices by
+calling `feature_names.index(feature_name)` inside a `for feature_name in
+categorical_features` loop. `feature_names` is a plain Python list built from
+the DataFrame column names. Each call to `.index()` performs a linear scan from
+position 0, making the total complexity O(C × F) where C is the number of
+categorical features and F is the total feature count.
+
+With wide feature-rich datasets (1000+ columns, 200+ categorical), this is called
+at every `fit()`, `predict()`, and `score()` invocation — directly on the hot
+path for model training and inference.
+
+## Root Cause
+
+```python
+# sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:440-443
+is_categorical = np.zeros(n_features, dtype=bool)
+feature_names = list(feature_names_in_)          # plain list
+for feature_name in categorical_features:
+    try:
+        is_categorical[feature_names.index(feature_name)] = True  # O(F) per cat feature
+    except ValueError as e:
+        ...
+```
+
+Fix: build a `dict` mapping name → index once before the loop.
+
+## Patch
+
+```python
+is_categorical = np.zeros(n_features, dtype=bool)
+feature_names = list(feature_names_in_)
+feature_name_to_idx = {name: i for i, name in enumerate(feature_names)}  # O(F) once
+for feature_name in categorical_features:
+    try:
+        is_categorical[feature_name_to_idx[feature_name]] = True           # O(1)
+    except KeyError:
+        raise ValueError(
+            f"categorical_features has an item value '{feature_name}' "
+            "which is not a valid feature name of the training "
+            f"data. Observed feature names: {feature_names}"
+        )
+```
+
+## Complexity Before
+
+**O(C × F)** — C categorical features × F total features per index lookup
+
+## Complexity After
+
+Build index once: **O(F)**, then **O(1)** per lookup → **O(F + C)** total
+
+## Reproduction
+
+```
+cd defects/sklearn/unit && javac -d . *.java && java -ea unit.SklearnTest
+```