Python/machine_learning/gaussian_naive_bayes.py

53 lines
1.4 KiB
Python
Raw Normal View History

# 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.
"""
# Load Iris dataset
iris = load_iris()
# Split dataset into train and test data
x = iris["data"] # features
y = iris["target"]
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.3, random_state=1
)
# Gaussian Naive Bayes
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
# 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...
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()