scikit-learn · betatim · May 28, 2025 · May 28, 2025 · May 28, 2025
diff --git a/examples/miscellaneous/plot_outlier_detection_bench.py b/examples/miscellaneous/plot_outlier_detection_bench.py
@@ -355,8 +355,7 @@ def fit_predict(estimator, X):
         ax=ax,
         plot_chance_level=(model_idx == len(n_neighbors_list) - 1),
         chance_level_kw={"linestyle": (0, (1, 10))},
-        linestyle=linestyle,
-        linewidth=2,
+        curve_kwargs=dict(linestyle=linestyle, linewidth=2),
     )
 _ = ax.set_title("RobustScaler with varying n_neighbors\non forestcover dataset")
 
@@ -395,8 +394,7 @@ def fit_predict(estimator, X):
         ax=ax,
         plot_chance_level=(model_idx == len(preprocessor_list) - 1),
         chance_level_kw={"linestyle": (0, (1, 10))},
-        linestyle=linestyle,
-        linewidth=2,
+        curve_kwargs=dict(linestyle=linestyle, linewidth=2),
     )
 _ = ax.set_title("Fixed n_neighbors with varying preprocessing\non forestcover dataset")
 
@@ -447,8 +445,7 @@ def fit_predict(estimator, X):
         ax=ax,
         plot_chance_level=(model_idx == len(preprocessor_list) - 1),
         chance_level_kw={"linestyle": (0, (1, 10))},
-        linestyle=linestyle,
-        linewidth=2,
+        curve_kwargs=dict(linestyle=linestyle, linewidth=2),
     )
 ax.set_title(
     "Fixed n_neighbors with varying preprocessing\non cardiotocography dataset"

diff --git a/examples/miscellaneous/plot_roc_curve_visualization_api.py b/examples/miscellaneous/plot_roc_curve_visualization_api.py
@@ -54,6 +54,8 @@
 rfc = RandomForestClassifier(n_estimators=10, random_state=42)
 rfc.fit(X_train, y_train)
 ax = plt.gca()
-rfc_disp = RocCurveDisplay.from_estimator(rfc, X_test, y_test, ax=ax, alpha=0.8)
-svc_disp.plot(ax=ax, alpha=0.8)
+rfc_disp = RocCurveDisplay.from_estimator(
+    rfc, X_test, y_test, ax=ax, curve_kwargs=dict(alpha=0.8)
+)
+svc_disp.plot(ax=ax, curve_kwargs=dict(alpha=0.8))
 plt.show()
diff --git a/examples/model_selection/plot_cost_sensitive_learning.py b/examples/model_selection/plot_cost_sensitive_learning.py
@@ -321,8 +321,7 @@ def plot_roc_pr_curves(vanilla_model, tuned_model, *, title):
             X_test,
             y_test,
             pos_label=pos_label,
-            linestyle=linestyle,
-            color=color,
+            curve_kwargs=dict(linestyle=linestyle, color=color),
             ax=axs[1],
             name=name,
             plot_chance_level=idx == 1,

diff --git a/examples/model_selection/plot_det.py b/examples/model_selection/plot_det.py
@@ -103,7 +103,12 @@
     )
     clf.fit(X_train, y_train)
     RocCurveDisplay.from_estimator(
-        clf, X_test, y_test, ax=ax_roc, name=name, color=color, linestyle=linestyle
+        clf,
+        X_test,
+        y_test,
+        ax=ax_roc,
+        name=name,
+        curve_kwargs=dict(color=color, linestyle=linestyle),
     )
     DetCurveDisplay.from_estimator(
         clf, X_test, y_test, ax=ax_det, name=name, color=color, linestyle=linestyle

diff --git a/examples/model_selection/plot_roc.py b/examples/model_selection/plot_roc.py
@@ -129,7 +129,7 @@
     y_onehot_test[:, class_id],
     y_score[:, class_id],
     name=f"{class_of_interest} vs the rest",
-    color="darkorange",
+    curve_kwargs=dict(color="darkorange"),
     plot_chance_level=True,
     despine=True,
 )
@@ -165,7 +165,7 @@
     y_onehot_test.ravel(),
     y_score.ravel(),
     name="micro-average OvR",
-    color="darkorange",
+    curve_kwargs=dict(color="darkorange"),
     plot_chance_level=True,
     despine=True,
 )
@@ -290,7 +290,7 @@
         y_onehot_test[:, class_id],
         y_score[:, class_id],
         name=f"ROC curve for {target_names[class_id]}",
-        color=color,
+        curve_kwargs=dict(color=color),
         ax=ax,
         plot_chance_level=(class_id == 2),
         despine=True,

diff --git a/examples/model_selection/plot_roc_crossval.py b/examples/model_selection/plot_roc_crossval.py
@@ -89,8 +89,7 @@
         X[test],
         y[test],
         name=f"ROC fold {fold}",
-        alpha=0.3,
-        lw=1,
+        curve_kwargs=dict(alpha=0.3, lw=1),
         ax=ax,
         plot_chance_level=(fold == n_splits - 1),
     )