Pushing the docs to dev/ for branch: main, commit 5fbd02e9022d4b4329fe93218999b03d394ecb8f

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -91,7 +91,7 @@
       },
       "outputs": [],
       "source": [
-        "from collections import defaultdict\n\nimport pandas as pd\n\nfrom sklearn.metrics import (\n    precision_score,\n    recall_score,\n    f1_score,\n    brier_score_loss,\n    log_loss,\n    roc_auc_score,\n)\n\nscores = defaultdict(list)\nfor i, (clf, name) in enumerate(clf_list):\n    clf.fit(X_train, y_train)\n    y_prob = clf.predict_proba(X_test)\n    y_pred = clf.predict(X_test)\n    scores[\"Classifier\"].append(name)\n\n    for metric in [brier_score_loss, log_loss]:\n        score_name = metric.__name__.replace(\"_\", \" \").replace(\"score\", \"\").capitalize()\n        scores[score_name].append(metric(y_test, y_prob[:, 1]))\n\n    for metric in [precision_score, recall_score, f1_score, roc_auc_score]:\n        score_name = metric.__name__.replace(\"_\", \" \").replace(\"score\", \"\").capitalize()\n        scores[score_name].append(metric(y_test, y_pred))\n\n    score_df = pd.DataFrame(scores).set_index(\"Classifier\")\n    score_df.round(decimals=3)\n\nscore_df"
+        "from collections import defaultdict\n\nimport pandas as pd\n\nfrom sklearn.metrics import (\n    precision_score,\n    recall_score,\n    f1_score,\n    brier_score_loss,\n    log_loss,\n    roc_auc_score,\n)\n\nscores = defaultdict(list)\nfor i, (clf, name) in enumerate(clf_list):\n    clf.fit(X_train, y_train)\n    y_prob = clf.predict_proba(X_test)\n    y_pred = clf.predict(X_test)\n    scores[\"Classifier\"].append(name)\n\n    for metric in [brier_score_loss, log_loss, roc_auc_score]:\n        score_name = metric.__name__.replace(\"_\", \" \").replace(\"score\", \"\").capitalize()\n        scores[score_name].append(metric(y_test, y_prob[:, 1]))\n\n    for metric in [precision_score, recall_score, f1_score]:\n        score_name = metric.__name__.replace(\"_\", \" \").replace(\"score\", \"\").capitalize()\n        scores[score_name].append(metric(y_test, y_pred))\n\n    score_df = pd.DataFrame(scores).set_index(\"Classifier\")\n    score_df.round(decimals=3)\n\nscore_df"
       ]
     },
     {
@@ -149,7 +149,7 @@
       },
       "outputs": [],
       "source": [
-        "scores = defaultdict(list)\nfor i, (clf, name) in enumerate(clf_list):\n    clf.fit(X_train, y_train)\n    y_prob = clf.predict_proba(X_test)\n    y_pred = clf.predict(X_test)\n    scores[\"Classifier\"].append(name)\n\n    for metric in [brier_score_loss, log_loss]:\n        score_name = metric.__name__.replace(\"_\", \" \").replace(\"score\", \"\").capitalize()\n        scores[score_name].append(metric(y_test, y_prob[:, 1]))\n\n    for metric in [precision_score, recall_score, f1_score, roc_auc_score]:\n        score_name = metric.__name__.replace(\"_\", \" \").replace(\"score\", \"\").capitalize()\n        scores[score_name].append(metric(y_test, y_pred))\n\n    score_df = pd.DataFrame(scores).set_index(\"Classifier\")\n    score_df.round(decimals=3)\n\nscore_df"
+        "scores = defaultdict(list)\nfor i, (clf, name) in enumerate(clf_list):\n    clf.fit(X_train, y_train)\n    y_prob = clf.predict_proba(X_test)\n    y_pred = clf.predict(X_test)\n    scores[\"Classifier\"].append(name)\n\n    for metric in [brier_score_loss, log_loss, roc_auc_score]:\n        score_name = metric.__name__.replace(\"_\", \" \").replace(\"score\", \"\").capitalize()\n        scores[score_name].append(metric(y_test, y_prob[:, 1]))\n\n    for metric in [precision_score, recall_score, f1_score]:\n        score_name = metric.__name__.replace(\"_\", \" \").replace(\"score\", \"\").capitalize()\n        scores[score_name].append(metric(y_test, y_pred))\n\n    score_df = pd.DataFrame(scores).set_index(\"Classifier\")\n    score_df.round(decimals=3)\n\nscore_df"
       ]
     },
     {

dev/_downloads/6d4f620ec6653356eb970c2a6ed62081/plot_calibration_curve.ipynb

@@ -155,11 +155,11 @@
     y_pred = clf.predict(X_test)
     scores["Classifier"].append(name)
 
-    for metric in [brier_score_loss, log_loss]:
+    for metric in [brier_score_loss, log_loss, roc_auc_score]:
         score_name = metric.__name__.replace("_", " ").replace("score", "").capitalize()
         scores[score_name].append(metric(y_test, y_prob[:, 1]))
 
-    for metric in [precision_score, recall_score, f1_score, roc_auc_score]:
+    for metric in [precision_score, recall_score, f1_score]:
         score_name = metric.__name__.replace("_", " ").replace("score", "").capitalize()
         scores[score_name].append(metric(y_test, y_pred))
 
@@ -300,11 +300,11 @@ def predict_proba(self, X):
     y_pred = clf.predict(X_test)
     scores["Classifier"].append(name)
 
-    for metric in [brier_score_loss, log_loss]:
+    for metric in [brier_score_loss, log_loss, roc_auc_score]:
         score_name = metric.__name__.replace("_", " ").replace("score", "").capitalize()
         scores[score_name].append(metric(y_test, y_prob[:, 1]))
 
-    for metric in [precision_score, recall_score, f1_score, roc_auc_score]:
+    for metric in [precision_score, recall_score, f1_score]:
         score_name = metric.__name__.replace("_", " ").replace("score", "").capitalize()
         scores[score_name].append(metric(y_test, y_pred))