scikit-learn · ArturoAmorQ · Aug 2, 2024 · Aug 2, 2024 · Aug 2, 2024 · Aug 8, 2024
diff --git a/examples/model_selection/plot_roc_crossval.py b/examples/model_selection/plot_roc_crossval.py
@@ -13,12 +13,11 @@
 better. The "steepness" of ROC curves is also important, since it is ideal to
 maximize the TPR while minimizing the FPR.
 
-This example shows the ROC response of different datasets, created from K-fold
-cross-validation. Taking all of these curves, it is possible to calculate the
-mean AUC, and see the variance of the curve when the
-training set is split into different subsets. This roughly shows how the
-classifier output is affected by changes in the training data, and how different
-the splits generated by K-fold cross-validation are from one another.
+This example demonstrates how the classifier's ROC response is influenced by
+variations in the training data as obtained through ShuffleSplit cross-validation.
+By analyzing all these curves, we can calculate the mean AUC and visualize the
+variability of the estimated curves across CV splits via a quantile-based
+region.
 
 .. note::
 
@@ -34,48 +33,45 @@
 # Load and prepare data
 # =====================
 #
-# We import the :ref:`iris_dataset` which contains 3 classes, each one
-# corresponding to a type of iris plant. One class is linearly separable from
-# the other 2; the latter are **not** linearly separable from each other.
-#
-# In the following we binarize the dataset by dropping the "virginica" class
-# (`class_id=2`). This means that the "versicolor" class (`class_id=1`) is
-# regarded as the positive class and "setosa" as the negative class
-# (`class_id=0`).
-
-import numpy as np
-
-from sklearn.datasets import load_iris
-
-iris = load_iris()
-target_names = iris.target_names
-X, y = iris.data, iris.target
-X, y = X[y != 2], y[y != 2]
-n_samples, n_features = X.shape
-
-# %%
-# We also add noisy features to make the problem harder.
-random_state = np.random.RandomState(0)
-X = np.concatenate([X, random_state.randn(n_samples, 200 * n_features)], axis=1)
+# We use :class:`~sklearn.datasets.make_classification` to generate a synthetic
+# dataset with 1,000 samples. The generated dataset has two classes by default.
+# In this case, we set a class separation factor of 0.5, making the classes
+# partially overlapping and not perfectly linearly separable.
+
+from sklearn.datasets import make_classification
+
+X, y = make_classification(
+    n_samples=1_000,
+    n_features=2,
+    n_redundant=0,
+    n_informative=2,
+    class_sep=0.5,
+    random_state=0,
+    n_clusters_per_class=1,
+)
 
 # %%
 # Classification and ROC analysis
 # -------------------------------
 #
-# Here we run a :class:`~sklearn.svm.SVC` classifier with cross-validation and
-# plot the ROC curves fold-wise. Notice that the baseline to define the chance
-# level (dashed ROC curve) is a classifier that would always predict the most
-# frequent class.
+# Here we run a :class:`~sklearn.ensemble.HistGradientBoostingClassifier`
+# classifier with cross-validation and plot the ROC curves fold-wise. Notice
+# that the baseline to define the chance level (dashed ROC curve) is a
+# classifier that would always predict the most frequent class.
+#
+# In the following plot, quantile coverage is represented in grey, though the
+# AUC value is reported in terms of the mean and standar deviation.
 
 import matplotlib.pyplot as plt
+import numpy as np
 
-from sklearn import svm
+from sklearn.ensemble import HistGradientBoostingClassifier
 from sklearn.metrics import RocCurveDisplay, auc
-from sklearn.model_selection import StratifiedKFold
+from sklearn.model_selection import StratifiedShuffleSplit
 
-n_splits = 6
-cv = StratifiedKFold(n_splits=n_splits)
-classifier = svm.SVC(kernel="linear", probability=True, random_state=random_state)
+n_splits = 30
+cv = StratifiedShuffleSplit(n_splits=n_splits, random_state=0)
+classifier = HistGradientBoostingClassifier(random_state=42)
 
 tprs = []
 aucs = []
@@ -88,11 +84,12 @@
         classifier,
         X[test],
         y[test],
-        name=f"ROC fold {fold}",
+        label=None,
         alpha=0.3,
         lw=1,
         ax=ax,
         plot_chance_level=(fold == n_splits - 1),
+        chance_level_kw={"label": None},
     )
     interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)
     interp_tpr[0] = 0.0
@@ -107,27 +104,27 @@
     mean_fpr,
     mean_tpr,
     color="b",
-    label=r"Mean ROC (AUC = %0.2f $\pm$ %0.2f)" % (mean_auc, std_auc),
+    label=rf"Mean ROC (AUC = {mean_auc:.2f} $\pm$ {std_auc:.2f})",
     lw=2,
     alpha=0.8,
 )
 
-std_tpr = np.std(tprs, axis=0)
-tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
-tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
+
+upper_quantile = np.quantile(tprs, 0.95, axis=0)
+lower_quantile = np.quantile(tprs, 0.05, axis=0)
 ax.fill_between(
     mean_fpr,
-    tprs_lower,
-    tprs_upper,
+    lower_quantile,
+    upper_quantile,
     color="grey",
-    alpha=0.2,
-    label=r"$\pm$ 1 std. dev.",
+    alpha=0.4,
+    label="5% to 95% percentile region",
 )
 
 ax.set(
     xlabel="False Positive Rate",
     ylabel="True Positive Rate",
-    title=f"Mean ROC curve with variability\n(Positive label '{target_names[1]}')",
+    title="Mean ROC curve with variability",
 )
 ax.legend(loc="lower right")
 plt.show()