Shannon Weiner Diversity Index Calculator

Analyze label spread across complex datasets with confidence. Track richness, evenness, and effective classes instantly. Turn raw counts into smarter training insights starting today.

Calculator Input Panel

Enter class labels and observed counts. The layout uses three columns on large screens, two on medium screens, and one on mobile.

Class 1
Class 2
Class 3
Class 4
Class 5
Class 6

Example Data Table

This sample reflects an imbalanced multiclass training dataset. You can load it into the form with one click.

Class Label Observed Count Use Case Note
Normal 420 Majority class in event monitoring.
Warning 210 Moderate signal category.
Fraud 85 Rare but important minority class.
Abuse 60 Small class with business risk.
Error 35 System fault examples.
Unknown 20 Low frequency fallback label.

Formula Used

Shannon Weiner Diversity Index:

H = - Σ (pᵢ × log(pᵢ))

Where pᵢ = nᵢ / N

nᵢ is the count of each class, and N is total samples.

Supporting metrics

  • Richness: Number of classes with positive counts.
  • Maximum entropy: log(S), where S is active classes.
  • Evenness: H / log(S), often called Pielou evenness.
  • Effective classes: base^H, which converts entropy into an intuitive class count.
  • Gini impurity: 1 - Σ(pᵢ²), useful for tree-based learning checks.

Why it matters in AI and machine learning

This index measures class uncertainty and spread. Higher values suggest broader distribution. Lower values reveal concentration, imbalance, or label dominance.

Use it during dataset review, drift tracking, stratified sampling checks, and active learning prioritization.

How to Use This Calculator

  1. Enter a dataset name for report clarity.
  2. Select the analysis mode that fits your workflow.
  3. Choose the logarithm base you prefer.
  4. Add one row for each class or label.
  5. Type the observed count for every class.
  6. Click Calculate diversity index.
  7. Review the summary metrics and contribution table.
  8. Use the chart and exports for audits or presentations.

Tip: Zero-count rows are ignored in the entropy calculation, but they remain visible during editing.

Frequently Asked Questions

1) What does the Shannon Weiner index measure?

It measures how evenly samples are distributed across classes. It rises when labels are more balanced and falls when one class dominates the dataset.

2) Why is this useful for machine learning datasets?

Balanced datasets often train more stable models. This index helps detect skewed label distributions before training, validation, or drift monitoring begins.

3) What is a good Shannon Weiner value?

There is no universal cutoff. Compare the result against maximum entropy, evenness, and past datasets. Context matters more than a single number.

4) Does the calculator support zero-count classes?

Yes. Zero-count rows stay in the form for planning, but they do not affect the entropy result because their probability is zero.

5) What is the difference between entropy and evenness?

Entropy measures diversity magnitude. Evenness rescales that value against the theoretical maximum, showing how close the distribution is to perfect balance.

6) Why include Gini impurity too?

Gini impurity is familiar in decision-tree workflows. Showing both metrics gives a broader picture of class concentration and label spread.

7) Which log base should I choose?

Natural log is common for ecological and statistical work. Base 2 is intuitive for information theory. Choose one and stay consistent.

8) Can I export the current analysis?

Yes. Use the CSV button for spreadsheets and the PDF button for reports, reviews, documentation, or stakeholder sharing.

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Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.