Understanding Learning Curve
Learning curves are graphical representations that illustrate how a model’s performance changes with increasing experience, typically measured by the amount of training data it has processed. The x-axis of a learning curve typically represents the amount of training data or the number of training iterations, while the y-axis represents the performance metric, such as error or accuracy.
It helps in diagnosing overfitting or underfitting by showing how the model’s error changes as it learns, guiding decisions on improving model training through adjustments in complexity or training data size.
Learning Curve To Identify Overfit & Underfit
A learning curve is a graphical representation showing how an increase in learning comes from greater experience. It can also reveal if a model is learning well, overfitting, or underfitting.
In this article, we’ll gain insights on how to identify underfitted and overfitted models using Learning Curve.
Table of Content
- Understanding Learning Curve
- Identifying Overfitting and Underfitting Using Learning Curves
- Implementation of Learning Curve To Identify Overfit & Underfit
- Learning Curve of a Well-fitted Model
- Learning Curve of an Overfit Model
- Learning Curve of an Underfit Model