scikit-learn · thomasjpfan · Apr 11, 2024 · Jan 2, 2024 · Jan 2, 2024 · Mar 28, 2024
diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst
@@ -347,19 +347,28 @@ Changelog
 
 - |Enhancement| :term:`CV splitters <CV splitter>` that ignores the group parameter now
   raises a warning when groups are passed in to :term:`split`. :pr:`28210` by
+  `Thomas Fan`_.
+
 - |Fix| the ``cv_results_`` attribute (of :class:`model_selection.GridSearchCV`) now
   returns masked arrays of the appropriate NumPy dtype, as opposed to always returning
   dtype ``object``. :pr:`28352` by :user:`Marco Gorelli<MarcoGorelli>`.
 
 - |Fix| :func:`sklearn.model_selection.train_test_score` works with Array API inputs.
   Previously indexing was not handled correctly leading to exceptions when using strict
   implementations of the Array API like CuPY.
-  :pr:`28407` by `Tim Head <betatim>`.
+  :pr:`28407` by :user:`Tim Head <betatim>`.
+
+- |Enhancement| The HTML diagram representation of
+  :class:`~model_selection.GridSearchCV`,
+  :class:`~model_selection.RandomizedSearchCV`,
+  :class:`~model_selection.HalvingGridSearchCV`, and
+  :class:`~model_selection.HalvingRandomSearchCV` will show the best estimator when
+  `refit=True`. :pr:`28722` by :user:`Yao Xiao <Charlie-XIAO>` and `Thomas Fan`_.
 
 :mod:`sklearn.multioutput`
 ..........................
 
-- |Enhancement| `chain_method` parameter added to `:class:`multioutput.ClassifierChain`.
+- |Enhancement| `chain_method` parameter added to :class:`multioutput.ClassifierChain`.
   :pr:`27700` by :user:`Lucy Liu <lucyleeow>`.
 
 :mod:`sklearn.neighbors`

diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py
@@ -33,6 +33,7 @@
     get_scorer_names,
 )
 from ..utils import Bunch, check_random_state
+from ..utils._estimator_html_repr import _VisualBlock
 from ..utils._param_validation import HasMethods, Interval, StrOptions
 from ..utils._tags import _safe_tags
 from ..utils.metadata_routing import (
@@ -1153,6 +1154,19 @@ def get_metadata_routing(self):
         )
         return router
 
+    def _sk_visual_block_(self):
+        if hasattr(self, "best_estimator_"):
+            key, estimator = "best_estimator_", self.best_estimator_
+        else:
+            key, estimator = "estimator", self.estimator
+
+        return _VisualBlock(
+            "parallel",
+            [estimator],
+            names=[f"{key}: {estimator.__class__.__name__}"],
+            name_details=[str(estimator)],
+        )
+
 
 class GridSearchCV(BaseSearchCV):
     """Exhaustive search over specified parameter values for an estimator.

diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
@@ -13,20 +13,23 @@
 import pytest
 from scipy.stats import bernoulli, expon, uniform
 
+from sklearn import config_context
 from sklearn.base import BaseEstimator, ClassifierMixin, is_classifier
 from sklearn.cluster import KMeans
 from sklearn.datasets import (
     make_blobs,
     make_classification,
     make_multilabel_classification,
 )
+from sklearn.dummy import DummyClassifier
 from sklearn.ensemble import HistGradientBoostingClassifier
 from sklearn.exceptions import FitFailedWarning
 from sklearn.experimental import enable_halving_search_cv  # noqa
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.impute import SimpleImputer
 from sklearn.linear_model import (
     LinearRegression,
+    LogisticRegression,
     Ridge,
     SGDClassifier,
 )
@@ -60,6 +63,7 @@
 from sklearn.naive_bayes import ComplementNB
 from sklearn.neighbors import KernelDensity, KNeighborsClassifier, LocalOutlierFactor
 from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
 from sklearn.svm import SVC, LinearSVC
 from sklearn.tests.metadata_routing_common import (
     ConsumingScorer,
@@ -2523,6 +2527,34 @@ def test_search_with_2d_array():
     np.testing.assert_array_equal(result.data, expected_data)
 
 
+def test_search_html_repr():
+    """Test different HTML representations for GridSearchCV."""
+    X, y = make_classification(random_state=42)
+
+    pipeline = Pipeline([("scale", StandardScaler()), ("clf", DummyClassifier())])
+    param_grid = {"clf": [DummyClassifier(), LogisticRegression()]}
+
+    # Unfitted shows the original pipeline
+    search_cv = GridSearchCV(pipeline, param_grid=param_grid, refit=False)
+    with config_context(display="diagram"):
+        repr_html = search_cv._repr_html_()
+        assert "<pre>DummyClassifier()</pre>" in repr_html
+
+    # Fitted with `refit=False` shows the original pipeline
+    search_cv.fit(X, y)
+    with config_context(display="diagram"):
+        repr_html = search_cv._repr_html_()
+        assert "<pre>DummyClassifier()</pre>" in repr_html
+
+    # Fitted with `refit=True` shows the best estimator
+    search_cv = GridSearchCV(pipeline, param_grid=param_grid, refit=True)
+    search_cv.fit(X, y)
+    with config_context(display="diagram"):
+        repr_html = search_cv._repr_html_()
+        assert "<pre>DummyClassifier()</pre>" not in repr_html
+        assert "<pre>LogisticRegression()</pre>" in repr_html
+
+
 # Metadata Routing Tests
 # ======================