In [1]:

---
execute:
  eval: false
  echo: true
  warning: false
format:
  html:
    code-fold: false
---

Importing libraries for the NB.

In [1]:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.naive_bayes import GaussianNB
from matplotlib.colors import ListedColormap
from sklearn.metrics import classification_report

Creating a synthetic dataset.

In [5]:

X, y = make_blobs(n_samples=300, centers=3, n_features=2, cluster_std=5.0, random_state=42)

Training the model.

In [6]:

gnb = GaussianNB()
gnb.fit(X, y)

GaussianNB()

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Printing the decision boundaries of the model.

In [7]:

h = .02
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))

Z = gnb.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

plt.figure(figsize=(10, 6))
plt.contourf(xx, yy, Z, alpha=0.3, cmap='viridis')
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', cmap='viridis')
plt.xlabel(r"$x_1$")
plt.ylabel(r"$x_2$")
plt.show()