Pushing the docs to dev/ for branch: main, commit 62389681eafb4609b70426c81b0223e8bcea9d2a

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: c9da83efc9c0f30360059804b16051bb
+config: cefd20e7a39e300ce7585bca4d46d63f
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -58,7 +58,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.metrics import PrecisionRecallDisplay\n\ndisplay = PrecisionRecallDisplay.from_estimator(\n    classifier, X_test, y_test, name=\"LinearSVC\"\n)\n_ = display.ax_.set_title(\"2-class Precision-Recall curve\")"
+        "from sklearn.metrics import PrecisionRecallDisplay\n\ndisplay = PrecisionRecallDisplay.from_estimator(\n    classifier, X_test, y_test, name=\"LinearSVC\", plot_chance_level=True\n)\n_ = display.ax_.set_title(\"2-class Precision-Recall curve\")"
       ]
     },
     {
@@ -76,7 +76,7 @@
       },
       "outputs": [],
       "source": [
-        "y_score = classifier.decision_function(X_test)\n\ndisplay = PrecisionRecallDisplay.from_predictions(y_test, y_score, name=\"LinearSVC\")\n_ = display.ax_.set_title(\"2-class Precision-Recall curve\")"
+        "y_score = classifier.decision_function(X_test)\n\ndisplay = PrecisionRecallDisplay.from_predictions(\n    y_test, y_score, name=\"LinearSVC\", plot_chance_level=True\n)\n_ = display.ax_.set_title(\"2-class Precision-Recall curve\")"
       ]
     },
     {
@@ -148,7 +148,7 @@
       },
       "outputs": [],
       "source": [
-        "display = PrecisionRecallDisplay(\n    recall=recall[\"micro\"],\n    precision=precision[\"micro\"],\n    average_precision=average_precision[\"micro\"],\n)\ndisplay.plot()\n_ = display.ax_.set_title(\"Micro-averaged over all classes\")"
+        "from collections import Counter\n\ndisplay = PrecisionRecallDisplay(\n    recall=recall[\"micro\"],\n    precision=precision[\"micro\"],\n    average_precision=average_precision[\"micro\"],\n    prevalence_pos_label=Counter(Y_test.ravel())[1] / Y_test.size,\n)\ndisplay.plot(plot_chance_level=True)\n_ = display.ax_.set_title(\"Micro-averaged over all classes\")"
       ]
     },
     {

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -142,7 +142,7 @@
 from sklearn.metrics import PrecisionRecallDisplay
 
 display = PrecisionRecallDisplay.from_estimator(
-    classifier, X_test, y_test, name="LinearSVC"
+    classifier, X_test, y_test, name="LinearSVC", plot_chance_level=True
 )
 _ = display.ax_.set_title("2-class Precision-Recall curve")
 
@@ -152,7 +152,9 @@
 # :func:`~sklearn.metrics.PrecisionRecallDisplay.from_predictions`.
 y_score = classifier.decision_function(X_test)
 
-display = PrecisionRecallDisplay.from_predictions(y_test, y_score, name="LinearSVC")
+display = PrecisionRecallDisplay.from_predictions(
+    y_test, y_score, name="LinearSVC", plot_chance_level=True
+)
 _ = display.ax_.set_title("2-class Precision-Recall curve")
 
 # %%
@@ -214,12 +216,15 @@
 # %%
 # Plot the micro-averaged Precision-Recall curve
 # ..............................................
+from collections import Counter
+
 display = PrecisionRecallDisplay(
     recall=recall["micro"],
     precision=precision["micro"],
     average_precision=average_precision["micro"],
+    prevalence_pos_label=Counter(Y_test.ravel())[1] / Y_test.size,
 )
-display.plot()
+display.plot(plot_chance_level=True)
 _ = display.ax_.set_title("Micro-averaged over all classes")
 
 # %%