Why Model Complexity is Important?
Finding the optimal model complexity is important because:
- Bias-Variance Tradeoff: Model complexity is closely related to the bias-variance tradeoff. Simple models may have high bias (systematic errors), while complex models may have high variance (sensitivity to small fluctuations in the training data). Finding the right level of complexity involves managing this tradeoff to achieve good predictive performance.
- Computational Resources: Complex models often require more computational resources for training and inference. The choice of model complexity may be influenced by practical considerations such as available computing power and time constraints.
- Interpretability: Simple models are often more interpretable, making it easier to understand and explain their decision-making processes. In some cases, interpretability is crucial, especially in sensitive applications where decisions impact individuals’ lives.
Model Complexity & Overfitting in Machine Learning
Model complexity leads to overfitting, which makes it harder to perform well on the unseen new data. In this article, we delve into the crucial challenges of model complexity and overfitting in machine learning.
Table of Content
- What is Model Complexity?
- Why Model Complexity is Important?
- What is Model Overfitting?
- How to Avoid Model Complexity and Overfitting?