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Calcimator

Overfitting Calculator

Calculate overfitting metrics, generalization gap, bias-variance trade-off, and model complexity analysis.

Inputs

thousand parameters

Results

Overfitting Gap

10%

Generalization Gap

10%

Variance10
Bias5
Complexity Ratio5
Optimal Complexity1,000 parameters
How to Use This Calculator
  1. Enter training loss and validation loss at each epoch.
  2. Review the generalization gap (validation loss − training loss) plotted over epochs.
  3. Identify the epoch where validation loss starts increasing while training loss continues to decrease.
  4. Set early stopping patience (number of epochs to wait before stopping) to that point.
  5. Apply regularization (L2, dropout, data augmentation) to reduce the overfitting gap.

How the result changes with Training Accuracy

Training AccuracyOverfitting GapGeneralization Gap
38%-47%-47%
133%48%48%
247%162%162%
342%257%257%

What each input means

Training Accuracy
Model accuracy on training set
Validation Accuracy
Model accuracy on validation set
Model Complexity
Number of model parameters
Dataset Size
Number of training samples
Regularization Strength
Regularization coefficient

How this is calculated

Formula

Overfitting Gap = Training Accuracy - Validation Accuracy

Worked example, using the default values

  1. Identify Input Parameters
    4 parameters
    Training Accuracy = 95, Validation Accuracy = 85, Model Complexity = 50, Dataset Size = 10000 = 5 input(s) provided
  2. Calculate Overfitting Gap
    Overfitting Gap
    10 = 10%
  3. Calculate Generalization Gap
    Generalization Gap
    10 = 10%
  4. Calculate Variance
    Variance
    10 = 10
  5. Calculate Bias
    Bias
    5 = 5

Engine last updated . Checked against 2 independently-derived tests — how we verify calculators.

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