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Update gaussian_naive_bayes.py (#7406)

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* Update gaussian_naive_bayes.py

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

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Sai Ganesh Manda 2022-10-19 17:13:26 +05:30 committed by GitHub
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1 changed files with 11 additions and 3 deletions
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machine_learning/gaussian_naive_bayes.py
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@ -1,7 +1,9 @@
# Gaussian Naive Bayes Example # Gaussian Naive Bayes Example
import time
from matplotlib import pyplot as plt from matplotlib import pyplot as plt
from sklearn.datasets import load_iris 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.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB from sklearn.naive_bayes import GaussianNB
@ -25,7 +27,9 @@ def main():
# Gaussian Naive Bayes # Gaussian Naive Bayes
nb_model = GaussianNB() 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 # Display Confusion Matrix
plot_confusion_matrix( plot_confusion_matrix(
@ -33,12 +37,16 @@ def main():
x_test, x_test,
y_test, y_test,
display_labels=iris["target_names"], display_labels=iris["target_names"],
cmap="Blues", cmap="Blues", # although, Greys_r has a better contrast...
normalize="true", normalize="true",
) )
plt.title("Normalized Confusion Matrix - IRIS Dataset") plt.title("Normalized Confusion Matrix - IRIS Dataset")
plt.show() 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__": if __name__ == "__main__":
main() main()