Learning Curve
Chart overview
A learning curve plots model performance metrics over training epochs or increasing training set sizes, comparing training and validation splits.
Key points
- It is the primary diagnostic tool for identifying overfitting, underfitting, and optimal stopping points in machine learning.
- Researchers use it routinely to justify model architecture choices and regularization strategies.
Example Visualization
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"Create a learning curve plot from my training history data. Plot training and validation metrics on the same axes with distinct colors, shade the gap between the two curves, mark the best validation epoch with a vertical dashed line, and add a legend."
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Python Code Example
Console Output
Figure saved: plotivy-learning-curve.png
Common Use Cases
- 1Diagnosing overfitting in deep learning image classifiers
- 2Selecting early stopping threshold for NLP fine-tuning runs
- 3Comparing convergence speed across different optimizers
- 4Demonstrating that adding more training data reduces generalization error
Pro Tips
Use a log scale on the y-axis when loss spans several orders of magnitude
Plot the standard deviation band across multiple runs to show training variance
Smooth noisy curves with a rolling average while still showing raw values faintly
Annotate the minimum validation loss point with its exact epoch and value
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.