Accuracy Curve Calculator

Accuracy Analysis Results

Results update after generating the curve.

Ready

Observations

0

Final training

—

Final validation

—

Best validation

—

Best point

—

Average validation

—

Generalisation gap

—

Target reached

—

Enter data or load an example, then select Generate Accuracy Curve.

Data and Analysis Controls

X-axis type

Accuracy input scale

Decimal precision

Target accuracy

Overfitting gap threshold

Early-stopping patience

Smoothing method

Smoothing window

EMA alpha

Duplicate handling

Missing values

Chart theme

Accuracy Data

Enter direct accuracy values or calculate a value using confusion-matrix counts.

Upload CSV

#	Epoch					Note	Action

Chart Options

Chart title

X-axis label

Y-axis label

Curve style

Line width

Marker size

Legend position

Chart height

Y-axis minimum

Y-axis maximum

Line pattern

Baseline accuracy

Processed Data

Epoch	Training	Validation	Testing	Comparison	Smoothed validation	Validation change	Gap	Status
No processed data yet.

Formula Used

Accuracy = (True Positives + True Negatives) ÷ (True Positives + True Negatives + False Positives + False Negatives)

Accuracy measures the proportion of correct predictions. It works best when classes are reasonably balanced. Always inspect precision and recall for uneven classes.

Generalisation Gap = Training Accuracy − Validation Accuracy

Improvement = Final Accuracy − Starting Accuracy

How to Use

Choose an X-axis type and input scale.
Enter accuracy values or upload a CSV file.
Select smoothing, target, and detection settings.
Customize the chart and visible series.
Generate the curve and review diagnostic results.
Export the chart, table, CSV, or PDF report.

Example Data

Epoch	Training	Validation	Testing
1	61	58	57
2	69	65	64
3	77	72	71
4	84	78	77
5	89	81	80

Frequently Asked Questions

What does an accuracy curve show?

It shows how model accuracy changes across epochs, iterations, thresholds, folds, or dataset sizes.

Why compare training and validation accuracy?

The comparison reveals whether learning transfers well to unseen data.

How does this calculator detect overfitting?

It checks for widening training-validation gaps and declining validation performance.

What is an early-stopping point?

It is the best validation point before performance stops improving for several observations.

Should accuracy be entered as decimals or percentages?

Either scale works. Choose the matching input option before generating results.

What smoothing methods are available?

The calculator supports simple, exponential, and weighted moving averages.

Can several models be compared?

Yes. Use the comparison column and rename each displayed series.

Why can accuracy mislead on imbalanced data?

A dominant class can produce high accuracy while minority predictions remain poor.

What CSV format is supported?

Use columns for X, training, validation, testing, comparison, and optional notes.