From c4bba8aad8094eda35f426a1b8f2ba14b7fb64ef Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=A9r=C3=B4me=20Delacotte?= <jerome.delacotte@bappli.com>
Date: Fri, 7 Nov 2025 18:58:04 +0100
Subject: [PATCH] Confusion_Matrix.py

---
 tools/sklearn/Confusion_Matrix.py | 47 +++++++++++++++++++++++++++++++
 1 file changed, 47 insertions(+)
 create mode 100644 tools/sklearn/Confusion_Matrix.py

diff --git a/tools/sklearn/Confusion_Matrix.py b/tools/sklearn/Confusion_Matrix.py
new file mode 100644
index 0000000..56d0177
--- /dev/null
+++ b/tools/sklearn/Confusion_Matrix.py
@@ -0,0 +1,47 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+# https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from sklearn import datasets, svm
+from sklearn.metrics import ConfusionMatrixDisplay
+from sklearn.model_selection import train_test_split
+
+# import some data to play with
+iris = datasets.load_iris()
+X = iris.data
+y = iris.target
+class_names = iris.target_names
+
+# Split the data into a training set and a test set
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+
+# Run classifier, using a model that is too regularized (C too low) to see
+# the impact on the results
+classifier = svm.SVC(kernel="linear", C=0.01).fit(X_train, y_train)
+
+np.set_printoptions(precision=2)
+
+# Plot non-normalized confusion matrix
+titles_options = [
+    ("Confusion matrix, without normalization", None),
+    ("Normalized confusion matrix", "true"),
+]
+for title, normalize in titles_options:
+    disp = ConfusionMatrixDisplay.from_estimator(
+        classifier,
+        X_test,
+        y_test,
+        display_labels=class_names,
+        cmap=plt.cm.Blues,
+        normalize=normalize,
+    )
+    disp.ax_.set_title(title)
+
+    print(title)
+    print(disp.confusion_matrix)
+
+plt.show()
\ No newline at end of file