From 9de53315dcd60f564a1eaa1887579cc433b2544d Mon Sep 17 00:00:00 2001
From: Rutu Bhanderi <140520663+rutubhanderi@users.noreply.github.com>
Date: Thu, 3 Oct 2024 23:45:29 +0530
Subject: [PATCH 1/2] Update gaussian_naive_bayes.py

The algorithm section now provides a clear overview of the steps the program follows, making it easier to understand the flow of the code.
---
 .../gaussian_naive_bayes.py.broken.txt            | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/machine_learning/gaussian_naive_bayes.py.broken.txt b/machine_learning/gaussian_naive_bayes.py.broken.txt
index 7e9a8d7f6dcf..727eacd15873 100644
--- a/machine_learning/gaussian_naive_bayes.py.broken.txt
+++ b/machine_learning/gaussian_naive_bayes.py.broken.txt
@@ -1,18 +1,24 @@
 # Gaussian Naive Bayes Example
 import time
-
 from matplotlib import pyplot as plt
 from sklearn.datasets import load_iris
 from sklearn.metrics import accuracy_score, plot_confusion_matrix
 from sklearn.model_selection import train_test_split
 from sklearn.naive_bayes import GaussianNB
 
-
 def main():
-
     """
     Gaussian Naive Bayes Example using sklearn function.
     Iris type dataset is used to demonstrate algorithm.
+    
+    Algorithm:
+    1. Load Dataset: Import the Iris dataset.
+    2. Data Preprocessing: Split dataset into features (X) and labels (y).
+    3. Model Initialization: Initialize Gaussian Naive Bayes model.
+    4. Model Training: Fit model using training data.
+    5. Make Predictions: Predict labels for the test dataset.
+    6. Evaluate Model: Calculate and display accuracy.
+    7. Visualize Results: Generate and display a normalized confusion matrix.
     """
 
     # Load Iris dataset
@@ -37,7 +43,7 @@ def main():
         x_test,
         y_test,
         display_labels=iris["target_names"],
-        cmap="Blues",  # although, Greys_r has a better contrast...
+        cmap="Blues",  # although Greys_r has better contrast...
         normalize="true",
     )
     plt.title("Normalized Confusion Matrix - IRIS Dataset")
@@ -47,6 +53,5 @@ def main():
     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 3a032ffa9f51d3aed9560c45e6d5da773bd8ed1a Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 3 Oct 2024 18:20:08 +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.broken.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/machine_learning/gaussian_naive_bayes.py.broken.txt b/machine_learning/gaussian_naive_bayes.py.broken.txt
index 727eacd15873..6be882854192 100644
--- a/machine_learning/gaussian_naive_bayes.py.broken.txt
+++ b/machine_learning/gaussian_naive_bayes.py.broken.txt
@@ -10,7 +10,7 @@ def main():
     """
     Gaussian Naive Bayes Example using sklearn function.
     Iris type dataset is used to demonstrate algorithm.
-    
+
     Algorithm:
     1. Load Dataset: Import the Iris dataset.
     2. Data Preprocessing: Split dataset into features (X) and labels (y).