AI & MACHINE LEARNING PROGRAM • LEVEL 08 — KNN & NAIVE BAYES

Calculate Gaussian Likelihood with Python

Learn calculate gaussian likelihood with python with a short, executable Python example.

IntermediateGaussian Naive BayesLikelihood

PROBLEM UNDERSTANDING

Input and expected output

Sample input

No input required

Sample output

0.242

COMPLETE PYTHON PROGRAM

Complete Python implementation

from math import exp, pi, sqrt
x, mean, variance = 1, 0, 1
likelihood = exp(-((x-mean)**2)/(2*variance)) / sqrt(2*pi*variance)
print(round(likelihood, 4))

CURRENT STEP

Select Start to walk through the important lines.

SELECTED LINE

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EXPECTED OUTPUT FOR THE SAMPLE

0.242

0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

Import the required standard-library tools and prepare the sample data for calculate gaussian likelihood.
Apply Gaussian Naive Bayes and Likelihood to compute the required result.
Display the result for calculate gaussian likelihood and compare it with the documented sample output.

This example of calculate gaussian likelihood computes the result directly from the prepared sample data. It demonstrates Gaussian Naive Bayes and Likelihood and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(n)

Auxiliary space

O(1)

DEBUGGING CHECKLIST

Common mistakes

Check this

For calculate gaussian likelihood, keep the data shape and value types consistent with Gaussian Naive Bayes.

Check this

Apply Gaussian Naive Bayes in the same order shown by the algorithm; changing the order can change the result.

Check this

Check denominators, numeric ranges and rounding before comparing the calculated value.

Try it yourself

Practice: Run the program with the sample input, predict its output, and then test one boundary case of your own.