Increasing the number of models in an ensemble (Model Ensemble) does not always lead to improved accuracy due to several limiting factors:
- Convergence of Predictions: Additional models may lead to similar predictions, resulting in minimal changes to the overall output.
- Limited Data Representation: If the dataset is noisy or incomplete, more models will only aggregate existing noise without capturing new patterns.
- Diminishing Returns: Each new model contributes less unique information, and performance is ultimately limited by the irreducible error in the data.
- Increased Complexity: More models increase computational costs and training times without necessarily improving accuracy.
- Overfitting Risk: Adding complex models can lead to overfitting, where the ensemble learns noise instead of underlying patterns.