Update gaussian_naive_bayes.py

machine_learning/gaussian_naive_bayes.py

@@ -1,7 +1,13 @@
 # Gaussian Naive Bayes Example
+import time
+
+import seaborn as sns  # For plotting a heatmap of the confusion matrix. This way will not throw any warning.
 from matplotlib import pyplot as plt
 from sklearn.datasets import load_iris
-from sklearn.metrics import plot_confusion_matrix
+from sklearn.metrics import (  # The plot_confusion_matrix method will be deprecated in the developed versions of Python 3.10.x according to the warning that it throws when this code is run.
+    accuracy_score,
+    confusion_matrix,
+)
 from sklearn.model_selection import train_test_split
 from sklearn.naive_bayes import GaussianNB
 
@@ -25,20 +31,22 @@ def main():
 
     # Gaussian Naive Bayes
     nb_model = GaussianNB()
-    nb_model.fit(x_train, y_train)
-
+    model_fit = nb_model.fit(x_train, y_train)
+    y_pred = model_fit.predict(x_test)
     # Display Confusion Matrix
-    plot_confusion_matrix(
-        nb_model,
-        x_test,
-        y_test,
-        display_labels=iris["target_names"],
-        cmap="Blues",
-        normalize="true",
-    )
-    plt.title("Normalized Confusion Matrix - IRIS Dataset")
+    Conf_Matrix = confusion_matrix(y_true=y_test, y_pred=y_pred)
+    sns.heatmap(data=Conf_Matrix, annot=True, cmap="Greys_r")
     plt.show()
 
+    # Printing the seen confusion matrix on the console
+    time.sleep(1.2)
+    print("The confusion matrix is:\n", Conf_Matrix)
+
+    time.sleep(1.8)
+    # Declaring the overall accuracy of the model
+    final_accuracy = 100 * accuracy_score(y_true=y_test, y_pred=y_pred)
+    print(f"The final accuracy of the model is: {round(final_accuracy, 2)}%")
+
 
 if __name__ == "__main__":
     main()