From 0379c6465cdb52967a5cb8cf03603f261d492e25 Mon Sep 17 00:00:00 2001
From: Sai Ganesh Manda <89340753+mvsg2@users.noreply.github.com>
Date: Tue, 18 Oct 2022 22:44:20 +0530
Subject: [PATCH 1/2] Update gaussian_naive_bayes.py

Just adding in a final metric of accuracy to declare...
---
 machine_learning/gaussian_naive_bayes.py | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/machine_learning/gaussian_naive_bayes.py b/machine_learning/gaussian_naive_bayes.py
index 77e7326626c4..e160749420f4 100644
--- a/machine_learning/gaussian_naive_bayes.py
+++ b/machine_learning/gaussian_naive_bayes.py
@@ -1,10 +1,10 @@
 # Gaussian Naive Bayes Example
 from matplotlib import pyplot as plt
 from sklearn.datasets import load_iris
-from sklearn.metrics import plot_confusion_matrix
+from sklearn.metrics import plot_confusion_matrix, accuracy_score
 from sklearn.model_selection import train_test_split
 from sklearn.naive_bayes import GaussianNB
-
+import time
 
 def main():
 
@@ -25,20 +25,25 @@ def main():
 
     # Gaussian Naive Bayes
     nb_model = GaussianNB()
-    nb_model.fit(x_train, y_train)
-
+    time.sleep(2.9)
+    model_fit = nb_model.fit(x_train, y_train)
+    y_pred = model_fit.predict(x_test) # Predictions on the test set
+    
     # Display Confusion Matrix
     plot_confusion_matrix(
         nb_model,
         x_test,
         y_test,
         display_labels=iris["target_names"],
-        cmap="Blues",
+        cmap="Blues", # although, Greys_r has a better contrast...
         normalize="true",
     )
     plt.title("Normalized Confusion Matrix - IRIS Dataset")
     plt.show()
-
+    
+    time.sleep(1.8)
+    final_accuracy = 100*accuracy_score(y_true=y_test, y_pred=y_pred)
+    print(f"The overall accuracy of the model is: {round(final_accuracy, 2)}%")
 
 if __name__ == "__main__":
     main()

From f3ec1919c933d0462f438677aa529104cd4ba013 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 18 Oct 2022 17:29:03 +0000
Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 machine_learning/gaussian_naive_bayes.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/machine_learning/gaussian_naive_bayes.py b/machine_learning/gaussian_naive_bayes.py
index e160749420f4..7e9a8d7f6dcf 100644
--- a/machine_learning/gaussian_naive_bayes.py
+++ b/machine_learning/gaussian_naive_bayes.py
@@ -1,10 +1,12 @@
 # Gaussian Naive Bayes Example
+import time
+
 from matplotlib import pyplot as plt
 from sklearn.datasets import load_iris
-from sklearn.metrics import plot_confusion_matrix, accuracy_score
+from sklearn.metrics import accuracy_score, plot_confusion_matrix
 from sklearn.model_selection import train_test_split
 from sklearn.naive_bayes import GaussianNB
-import time
+
 
 def main():
 
@@ -27,23 +29,24 @@ def main():
     nb_model = GaussianNB()
     time.sleep(2.9)
     model_fit = nb_model.fit(x_train, y_train)
-    y_pred = model_fit.predict(x_test) # Predictions on the test set
-    
+    y_pred = model_fit.predict(x_test)  # Predictions on the test set
+
     # Display Confusion Matrix
     plot_confusion_matrix(
         nb_model,
         x_test,
         y_test,
         display_labels=iris["target_names"],
-        cmap="Blues", # although, Greys_r has a better contrast...
+        cmap="Blues",  # although, Greys_r has a better contrast...
         normalize="true",
     )
     plt.title("Normalized Confusion Matrix - IRIS Dataset")
     plt.show()
-    
+
     time.sleep(1.8)
-    final_accuracy = 100*accuracy_score(y_true=y_test, y_pred=y_pred)
+    final_accuracy = 100 * accuracy_score(y_true=y_test, y_pred=y_pred)
     print(f"The overall accuracy of the model is: {round(final_accuracy, 2)}%")
 
+
 if __name__ == "__main__":
     main()