Identifying Overfitting and Underfitting Using Learning Curves
Learning curves visually depict the model’s performance on both the training and validation sets over time. By analyzing these curves, we can identify overfitting and underfitting:
- Overfitting:
- The training accuracy is high and remains stable or even increases.
- The validation accuracy is significantly lower than the training accuracy and may even decrease over time.
- This indicates that the model is memorizing the training data instead of learning the general patterns.
- Underfitting:
- Both the training and validation accuracies are low and remain relatively constant.
- This suggests that the model is unable to capture the essential features of the data, leading to poor performance on both sets.
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