Hinge Loss Curve Calculator

Calculation summary

No calculation yet

Interactive visualizations

Data and calculation settings

Example dataset

Dataset rows

Use CSV columns: actual, score, weight, model, split, epoch, regularization. Header rows are optional.

Upload CSV

Uploaded content replaces the current rows.

Paste interpretation

Spreadsheet rows and ordinary CSV text are supported.

Classification mode

Positive class

Used for 0/1 conversion and one-versus-rest mapping.

0/1 label handling

Loss function

Margin threshold

Loss aggregation

C multiplier

Regularization lambda

Parameter norm squared

Used to estimate the regularized objective.

Curve range and display

Minimum decision score

Maximum decision score

Score step

Class curves

Curve shape

Loss scale

Show observation markers

Show average-loss reference

Show chart grid

Show chart legend

Show margin annotations

Dark graph mode

Graph customization

Chart title

Horizontal-axis label

Vertical-axis label

Curve thickness

Marker size

Marker symbol

Positive-class color

Negative-class color

Margin-line color

Graph background

Legend position

Decimal precision

Chart height

Custom y-axis maximum

Results rows per page

Model diagnostics

Diagnostic findings

Class and split comparison

Detailed results table

					Prediction	Classification	Margin status				Model	Split

Export and sharing

Formula used

Standard hinge loss: L_i = max(0, m − y_if(x_i))
Squared hinge loss: L_i = max(0, m − y_if(x_i))²
Modified Huber: 0 when margin ≥ m; (m − margin)² inside the transition; linear beyond the negative margin.
Regularized objective: C × aggregated loss + ½λ||w||²

How to use

Enter labels, decision scores, weights, models, and dataset splits.
Select a hinge-family loss and adjust the margin threshold.
Choose curve ranges, graph styling, precision, and visibility controls.
Calculate the results and inspect margin violations or misclassifications.
Switch visualization tabs to compare classes, epochs, and models.
Export the chart, table, graph data, or printable report.

Worked example

Actual label	Decision score	Signed margin	Standard loss	Interpretation
+1	1.6	1.6	0	Correct and outside the margin
+1	0.4	0.4	0.6	Correct but inside the margin
−1	0.3	−0.3	1.3	Misclassified with a larger penalty

Interpretation guide

Zero loss: The sample is correctly classified beyond the required margin.
Positive loss: The sample violates the margin or is misclassified.
Signed margin: Positive values indicate correct classification direction.
Large negative margin: The prediction is confidently wrong.
Sample weight: Larger weights increase that observation's contribution.
Squared hinge: Larger violations receive substantially stronger penalties.
Regularization: Larger values favor simpler parameter configurations.

Frequently asked questions

What is hinge loss?

Hinge loss penalizes incorrect classifications and insufficient classification margins.

Why can a correct prediction have loss?

A prediction can be correct but remain inside the required margin.

Which models commonly use hinge loss?

Support vector machines and large-margin linear classifiers commonly use it.

What does the margin threshold control?

It sets the signed-margin level required for zero loss.

How is squared hinge loss different?

It squares violations and emphasizes samples with larger errors.

Can this calculator handle imbalanced classes?

Yes. Use sample weights to increase minority-class influence.

How does one-versus-rest mode work?

The chosen class becomes positive and every other class becomes negative.

What do negative signed margins mean?

They indicate that the predicted direction disagrees with the true class.

Can several models be compared?

Yes. Add model names to rows and open model comparison.