What is model complexity in machine learning?

What is model complexity in machine learning?

Model complexity refers to the capacity of a machine learning model to fit a wide variety of functions. It can be influenced by factors like the number of parameters, the structure of the model, and the amount of training data.

How does model complexity affect overfitting?

Higher model complexity can lead to overfitting, where the model learns the noise in the training data instead of the underlying pattern, resulting in poor generalization to new data.

What are some ways to manage model complexity?

You can manage model complexity through techniques like regularization, pruning, and using simpler models. Cross-validation can also help in selecting the right model complexity.

Can you give examples of regularization techniques?

Sure! Common regularization techniques include L1 regularization (Lasso), L2 regularization (Ridge), and Elastic Net, which combines both L1 and L2.

What is the bias-variance tradeoff?

The bias-variance tradeoff is the balance between a model's ability to minimize bias (error due to assumptions) and variance (error due to sensitivity to fluctuations in the training set). A good model seeks to minimize both.