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Naive Bayes Gaussian Classification

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!uv pip install -q \
    numpy==2.3.2 \
    matplotlib==3.10.6 \
    seaborn==0.13.2 \
    scikit-learn==1.7.1

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import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import make_blobs

from sklearn.naive_bayes import GaussianNB

%matplotlib inline

sns.set_style("darkgrid")
sns.set_theme(style="darkgrid")


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X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5)

x_axis = X[:, 0]  # take all rows, first column
y_axis = X[:, 1]  # take all rows, second column

plt.scatter(
    x_axis,
    y_axis,
    c=y,  # colors according to labels
    s=50,  # marker size
    cmap="RdBu",  # color map, Red & Blue
)
plt.show()
output_3_0.png

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

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GaussianNB()

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GaussianNB

?Documentation for GaussianNBiFitted

Parameters
priors None
var_smoothing 1e-09 
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rng = np.random.RandomState(0)
Xnew = [-6, -14] + [14, 18] * rng.rand(2000, 2)

Xnew[:2, :]

array([[ 1.68338905, -1.12659141], [ 2.43868727, -4.19210271]])

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ynew = model.predict(Xnew)


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plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap="RdBu")
lim = plt.axis()
plt.scatter(Xnew[:, 0], Xnew[:, 1], c=ynew, s=20, cmap="RdBu", alpha=0.1)
plt.show()
output_7_0.png

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yprob = model.predict_proba(Xnew)
yprob[-8:].round(2)

array([[0.89, 0.11], [1. , 0. ], [1. , 0. ], [1. , 0. ], [1. , 0. ], [1. , 0. ], [0. , 1. ], [0.15, 0.85]])