What is model complexity in machine learning?

What is model complexity in machine learning?

Model complexity refers to the capacity of a model to fit a wide variety of functions. It can be influenced by the number of parameters, the structure of the model, and the type of algorithms used.

Why is model complexity important?

It's crucial because it affects the model's ability to generalize. A model that's too complex may overfit the training data, while a model that's too simple may underfit.

How can I determine the right level of complexity for my model?

You can use techniques like cross-validation, learning curves, and regularization methods to find the right balance between bias and variance.

What are some common pitfalls of high model complexity?

Common pitfalls include overfitting, increased computational cost, and difficulty in interpreting the model's predictions.