From 80ff25ed38e62bcf2e51a4a51bf7bf8f9b03ea11 Mon Sep 17 00:00:00 2001
From: Sai Ganesh Manda <89340753+mvsg2@users.noreply.github.com>
Date: Wed, 19 Oct 2022 17:13:26 +0530
Subject: [PATCH] Update gaussian_naive_bayes.py (#7406)

* Update gaussian_naive_bayes.py

Just adding in a final metric of accuracy to declare...

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 machine_learning/gaussian_naive_bayes.py | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/machine_learning/gaussian_naive_bayes.py b/machine_learning/gaussian_naive_bayes.py
index 77e732662..7e9a8d7f6 100644
--- a/machine_learning/gaussian_naive_bayes.py
+++ b/machine_learning/gaussian_naive_bayes.py
@@ -1,7 +1,9 @@
 # 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
+from sklearn.metrics import accuracy_score, plot_confusion_matrix
 from sklearn.model_selection import train_test_split
 from sklearn.naive_bayes import GaussianNB
 
@@ -25,7 +27,9 @@ 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(
@@ -33,12 +37,16 @@ def main():
         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()