In Artificial Intelligence, the Generalization Curve is a graphical representation depicting an ML model’s performance on the training and validation datasets plotted as a function of the complexity and the amount of data used for training.
The curve is used to plot two performance metrics on the X and Y axis. The X-axis denotes the size of the training data set and the model complexity. The Y axis indicates performance metrics such as recall and accuracy.
The model’s performance may improve if the dataset’s complexity and size increase. However, the performance of the validation dataset may degrade, resulting in overfitting.
The generalization curve captures this imbalance between overfitting and underfitting by identifying the “sweet spot,” which is the point where the model achieves equilibrium between variance and bias. This sweet spot represents the optimum balance between validation accuracy and training, indicating the model’s good generalization.
The generalization curve helps data scientists arrive at informed conclusions about model complexity and the size of the training dataset.
Applications of Generalization Curve in AI
1. Model selection
One of the primary uses of the generalization curve is to help select the best model that best achieves the balance between variance and bias and also delivers the best performance on new and unknown data.
2. Hyperparameter tuning
The generalization curve helps identify the optimal parametric values like:
- Regularization strength
- Learning rate
- Number of layers
3. Early stopping
Early stopping is a technique facilitated by the generalization curve to stop the training process with increasing validation errors. This also prevents overfitting.
4. Bias-Variance Analysis
In bias-variance analysis, the gap between training and variation curves determines whether the model is underfitted or overfitted. A large gap denotes overfitting, and a small one indicates underfitting.
5. Learning curves
Generalization curves are used to construct learning curves that depict how an ML model performs based on the amount of training data. The learning curves can also help diagnose underfitting or overfitting.
Related terms
Early Stopping
Generalization
