How does model complexity affect AI performance?

How does model complexity affect AI performance?

Model complexity refers to the number of parameters and the structure of the model. Generally, more complex models can capture intricate patterns in data, but they also risk overfitting.

What is overfitting, and how can it be avoided?

Overfitting occurs when a model learns the training data too well, including noise, leading to poor performance on new data. It can be avoided by using techniques like cross-validation, regularization, and pruning.

Are there any trade-offs between model complexity and interpretability?

Yes, typically, more complex models are harder to interpret. Simpler models are easier to understand but may not capture all the nuances of the data.

Can you give examples of simple vs complex models?

Sure! A linear regression model is a simple model, while a deep neural network with multiple layers is a complex model. The choice depends on the problem at hand.