What factors contribute to the complexity of machine learning models?

What factors contribute to the complexity of machine learning models?

The complexity of machine learning models can be influenced by several factors, including the number of features, the type of algorithms used, and the amount of training data.

How does the number of features affect model complexity?

More features can increase model complexity, as the model has to learn from a larger set of inputs, which can lead to overfitting.

What is overfitting, and how can it be avoided?

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern. It can be avoided by using techniques like cross-validation, regularization, and pruning.

Are simpler models always better?

Not necessarily. While simpler models are easier to interpret and less prone to overfitting, they may not capture complex patterns in the data as effectively as more complex models.