Gaussian Naive Bayes Score Calculator
Combine entered independent Gaussian feature models into a binary-class posterior score.
YOUR CALCULATION
Result
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Calculation steps
Work through one step at a time or reveal the complete calculation. The steps use your current inputs.
How to calculate
Sum Gaussian log densities and use a stable logistic normalization of the two class scores.
Log score difference=log(prior1/prior0)+Σ(log density1−log density0)
Worked example
Equal priors and identical feature distributions give posterior class1 probability0.5.
Assumptions and input details
Entered models only, conditional feature independence, positive SDs and two classes. No model fitting or calibration/accuracy guarantee; correlated features can distort the score.
Using this calculator
Can I enter my own problem?
Yes. Replace the example values with your own inputs. The result, calculation steps, tables and applicable diagrams update from those inputs. Blank or invalid values clear the result; example values are never silently substituted for missing inputs.
Are the results exact?
Results use floating-point arithmetic and displayed rounding. Read the formula, assumptions and any stated data precision before interpreting the result.
Is my input uploaded?
The calculation runs in your browser. No account, file upload or external AI service is required. Inputs are not saved between page reloads.
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