scikit-learn · adrinjalali · May 7, 2025 · May 7, 2025 · May 7, 2025 · May 7, 2025
diff --git a/doc/modules/classification_threshold.rst b/doc/modules/classification_threshold.rst
@@ -38,8 +38,8 @@ probability estimates :math:`P(y|X)` and class labels::
     >>> classifier.predict_proba(X[:4])
     array([[0.94     , 0.06     ],
            [0.94     , 0.06     ],
-           [0.0416..., 0.9583...],
-           [0.0416..., 0.9583...]])
+           [0.0416, 0.9583],
+           [0.0416, 0.9583]])
     >>> classifier.predict(X[:4])
     array([0, 0, 1, 1])
 
@@ -112,10 +112,10 @@ a meaningful metric for their use case.
         >>> base_model = LogisticRegression()
         >>> model = TunedThresholdClassifierCV(base_model, scoring=scorer)
         >>> scorer(model.fit(X, y), X, y)
-        0.88...
+        0.88
         >>> # compare it with the internal score found by cross-validation
         >>> model.best_score_
-        np.float64(0.86...)
+        np.float64(0.86)
 
 Important notes regarding the internal cross-validation
 -------------------------------------------------------

diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst
@@ -1310,32 +1310,32 @@ ignoring permutations::
   >>> labels_true = [0, 0, 0, 1, 1, 1]
   >>> labels_pred = [0, 0, 1, 1, 2, 2]
   >>> metrics.rand_score(labels_true, labels_pred)
-  0.66...
+  0.66
 
 The Rand index does not ensure to obtain a value close to 0.0 for a
 random labelling. The adjusted Rand index **corrects for chance** and
 will give such a baseline.
 
   >>> metrics.adjusted_rand_score(labels_true, labels_pred)
-  0.24...
+  0.24
 
 As with all clustering metrics, one can permute 0 and 1 in the predicted
 labels, rename 2 to 3, and get the same score::
 
   >>> labels_pred = [1, 1, 0, 0, 3, 3]
   >>> metrics.rand_score(labels_true, labels_pred)
-  0.66...
+  0.66
   >>> metrics.adjusted_rand_score(labels_true, labels_pred)
-  0.24...
+  0.24
 
 Furthermore, both :func:`rand_score` and :func:`adjusted_rand_score` are
 **symmetric**: swapping the argument does not change the scores. They can
 thus be used as **consensus measures**::
 
   >>> metrics.rand_score(labels_pred, labels_true)
-  0.66...
+  0.66
   >>> metrics.adjusted_rand_score(labels_pred, labels_true)
-  0.24...
+  0.24
 
 Perfect labeling is scored 1.0::
 
@@ -1353,9 +1353,9 @@ will not necessarily be close to zero::
   >>> labels_true = [0, 0, 0, 0, 0, 0, 1, 1]
   >>> labels_pred = [0, 1, 2, 3, 4, 5, 5, 6]
   >>> metrics.rand_score(labels_true, labels_pred)
-  0.39...
+  0.39
   >>> metrics.adjusted_rand_score(labels_true, labels_pred)
-  -0.07...
+  -0.072
 
 
 .. topic:: Advantages:
@@ -1466,21 +1466,21 @@ proposed more recently and is **normalized against chance**::
   >>> labels_pred = [0, 0, 1, 1, 2, 2]
 
   >>> metrics.adjusted_mutual_info_score(labels_true, labels_pred)  # doctest: +SKIP
-  0.22504...
+  0.22504
 
 One can permute 0 and 1 in the predicted labels, rename 2 to 3 and get
 the same score::
 
   >>> labels_pred = [1, 1, 0, 0, 3, 3]
   >>> metrics.adjusted_mutual_info_score(labels_true, labels_pred)  # doctest: +SKIP
-  0.22504...
+  0.22504
 
 All, :func:`mutual_info_score`, :func:`adjusted_mutual_info_score` and
 :func:`normalized_mutual_info_score` are symmetric: swapping the argument does
 not change the score. Thus they can be used as a **consensus measure**::
 
   >>> metrics.adjusted_mutual_info_score(labels_pred, labels_true)  # doctest: +SKIP
-  0.22504...
+  0.22504
 
 Perfect labeling is scored 1.0::
 
@@ -1494,14 +1494,14 @@ Perfect labeling is scored 1.0::
 This is not true for ``mutual_info_score``, which is therefore harder to judge::
 
   >>> metrics.mutual_info_score(labels_true, labels_pred)  # doctest: +SKIP
-  0.69...
+  0.69
 
 Bad (e.g. independent labelings) have non-positive scores::
 
   >>> labels_true = [0, 1, 2, 0, 3, 4, 5, 1]
   >>> labels_pred = [1, 1, 0, 0, 2, 2, 2, 2]
   >>> metrics.adjusted_mutual_info_score(labels_true, labels_pred)  # doctest: +SKIP
-  -0.10526...
+  -0.10526
 
 
 .. topic:: Advantages:
@@ -1649,16 +1649,16 @@ We can turn those concept as scores :func:`homogeneity_score` and
   >>> labels_pred = [0, 0, 1, 1, 2, 2]
 
   >>> metrics.homogeneity_score(labels_true, labels_pred)
-  0.66...
+  0.66
 
   >>> metrics.completeness_score(labels_true, labels_pred)
-  0.42...
+  0.42
 
 Their harmonic mean called **V-measure** is computed by
 :func:`v_measure_score`::
 
   >>> metrics.v_measure_score(labels_true, labels_pred)
-  0.51...
+  0.516
 
 This function's formula is as follows:
 
@@ -1667,12 +1667,12 @@ This function's formula is as follows:
 `beta` defaults to a value of 1.0, but for using a value less than 1 for beta::
 
   >>> metrics.v_measure_score(labels_true, labels_pred, beta=0.6)
-  0.54...
+  0.547
 
 more weight will be attributed to homogeneity, and using a value greater than 1::
 
   >>> metrics.v_measure_score(labels_true, labels_pred, beta=1.8)
-  0.48...
+  0.48
 
 more weight will be attributed to completeness.
 
@@ -1683,14 +1683,14 @@ Homogeneity, completeness and V-measure can be computed at once using
 :func:`homogeneity_completeness_v_measure` as follows::
 
   >>> metrics.homogeneity_completeness_v_measure(labels_true, labels_pred)
-  (0.66..., 0.42..., 0.51...)
+  (0.67, 0.42, 0.52)
 
 The following clustering assignment is slightly better, since it is
 homogeneous but not complete::
 
   >>> labels_pred = [0, 0, 0, 1, 2, 2]
   >>> metrics.homogeneity_completeness_v_measure(labels_true, labels_pred)
-  (1.0, 0.68..., 0.81...)
+  (1.0, 0.68, 0.81)
 
 .. note::
 
@@ -1820,15 +1820,15 @@ between two clusters.
   >>> labels_pred = [0, 0, 1, 1, 2, 2]
 
   >>> metrics.fowlkes_mallows_score(labels_true, labels_pred)
-  0.47140...
+  0.47140
 
 One can permute 0 and 1 in the predicted labels, rename 2 to 3 and get
 the same score::
 
   >>> labels_pred = [1, 1, 0, 0, 3, 3]
 
   >>> metrics.fowlkes_mallows_score(labels_true, labels_pred)
-  0.47140...
+  0.47140
 
 Perfect labeling is scored 1.0::
 
@@ -1917,7 +1917,7 @@ cluster analysis.
   >>> kmeans_model = KMeans(n_clusters=3, random_state=1).fit(X)
   >>> labels = kmeans_model.labels_
   >>> metrics.silhouette_score(X, labels, metric='euclidean')
-  0.55...
+  0.55
 
 .. topic:: Advantages:
 
@@ -1974,7 +1974,7 @@ cluster analysis:
   >>> kmeans_model = KMeans(n_clusters=3, random_state=1).fit(X)
   >>> labels = kmeans_model.labels_
   >>> metrics.calinski_harabasz_score(X, labels)
-  561.59...
+  561.59
 
 
 .. topic:: Advantages:
@@ -2048,7 +2048,7 @@ cluster analysis as follows:
   >>> kmeans = KMeans(n_clusters=3, random_state=1).fit(X)
   >>> labels = kmeans.labels_
   >>> davies_bouldin_score(X, labels)
-  0.666...
+  0.666
 
 
 .. topic:: Advantages:

diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst
@@ -504,10 +504,10 @@ on data type or column name::
   ...       OneHotEncoder(),
   ...       make_column_selector(pattern='city', dtype_include=object))])
   >>> ct.fit_transform(X)
-  array([[ 0.904...,  0.      ,  1. ,  0. ,  0. ],
-         [-1.507...,  1.414...,  1. ,  0. ,  0. ],
-         [-0.301...,  0.      ,  0. ,  1. ,  0. ],
-         [ 0.904..., -1.414...,  0. ,  0. ,  1. ]])
+  array([[ 0.904,  0.      ,  1. ,  0. ,  0. ],
+         [-1.507,  1.414,  1. ,  0. ,  0. ],
+         [-0.301,  0.      ,  0. ,  1. ,  0. ],
+         [ 0.904, -1.414,  0. ,  0. ,  1. ]])
 
 Strings can reference columns if the input is a DataFrame, integers are always
 interpreted as the positional columns.
@@ -571,9 +571,9 @@ will use the column names to select the columns::
   >>> X_new = pd.DataFrame({"expert_rating": [5, 6, 1],
   ...                       "ignored_new_col": [1.2, 0.3, -0.1]})
   >>> ct.transform(X_new)
-  array([[ 0.9...],
-         [ 2.1...],
-         [-3.9...]])
+  array([[ 0.9],
+         [ 2.1],
+         [-3.9]])
 
 .. _visualizing_composite_estimators:
 

diff --git a/doc/modules/cross_validation.rst b/doc/modules/cross_validation.rst
@@ -55,7 +55,7 @@ data for testing (evaluating) our classifier::
 
   >>> clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train)
   >>> clf.score(X_test, y_test)
-  0.96...
+  0.96
 
 When evaluating different settings ("hyperparameters") for estimators,
 such as the ``C`` setting that must be manually set for an SVM,
@@ -120,7 +120,7 @@ time)::
   >>> clf = svm.SVC(kernel='linear', C=1, random_state=42)
   >>> scores = cross_val_score(clf, X, y, cv=5)
   >>> scores
-  array([0.96..., 1. , 0.96..., 0.96..., 1. ])
+  array([0.96, 1. , 0.96, 0.96, 1. ])
 
 The mean score and the standard deviation are hence given by::
 
@@ -135,7 +135,7 @@ scoring parameter::
   >>> scores = cross_val_score(
   ...     clf, X, y, cv=5, scoring='f1_macro')
   >>> scores
-  array([0.96..., 1.  ..., 0.96..., 0.96..., 1.        ])
+  array([0.96, 1., 0.96, 0.96, 1.])
 
 See :ref:`scoring_parameter` for details.
 In the case of the Iris dataset, the samples are balanced across target
@@ -153,7 +153,7 @@ validation iterator instead, for instance::
   >>> n_samples = X.shape[0]
   >>> cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=0)
   >>> cross_val_score(clf, X, y, cv=cv)
-  array([0.977..., 0.977..., 1.  ..., 0.955..., 1.        ])
+  array([0.977, 0.977, 1., 0.955, 1.])
 
 Another option is to use an iterable yielding (train, test) splits as arrays of
 indices, for example::
@@ -168,7 +168,7 @@ indices, for example::
   ...
   >>> custom_cv = custom_cv_2folds(X)
   >>> cross_val_score(clf, X, y, cv=custom_cv)
-  array([1.        , 0.973...])
+  array([1.        , 0.973])
 
 .. dropdown:: Data transformation with held-out data
 
@@ -185,15 +185,15 @@ indices, for example::
     >>> clf = svm.SVC(C=1).fit(X_train_transformed, y_train)
     >>> X_test_transformed = scaler.transform(X_test)
     >>> clf.score(X_test_transformed, y_test)
-    0.9333...
+    0.9333
 
   A :class:`Pipeline <sklearn.pipeline.Pipeline>` makes it easier to compose
   estimators, providing this behavior under cross-validation::
 
     >>> from sklearn.pipeline import make_pipeline
     >>> clf = make_pipeline(preprocessing.StandardScaler(), svm.SVC(C=1))
     >>> cross_val_score(clf, X, y, cv=cv)
-    array([0.977..., 0.933..., 0.955..., 0.933..., 0.977...])
+    array([0.977, 0.933, 0.955, 0.933, 0.977])
 
   See :ref:`combining_estimators`.
 
@@ -237,7 +237,7 @@ predefined scorer names::
     >>> sorted(scores.keys())
     ['fit_time', 'score_time', 'test_precision_macro', 'test_recall_macro']
     >>> scores['test_recall_macro']
-    array([0.96..., 1.  ..., 0.96..., 0.96..., 1.        ])
+    array([0.96, 1., 0.96, 0.96, 1.])
 
 Or as a dict mapping scorer name to a predefined or custom scoring function::
 
@@ -250,7 +250,7 @@ Or as a dict mapping scorer name to a predefined or custom scoring function::
     ['fit_time', 'score_time', 'test_prec_macro', 'test_rec_macro',
      'train_prec_macro', 'train_rec_macro']
     >>> scores['train_rec_macro']
-    array([0.97..., 0.97..., 0.99..., 0.98..., 0.98...])
+    array([0.97, 0.97, 0.99, 0.98, 0.98])
 
 Here is an example of ``cross_validate`` using a single metric::