model complexity

Model complexity refers to how detailed or flexible a machine learning model is in capturing patterns in data. A simple model, like a straight line, can only represent basic relationships, while a complex one can fit intricate patterns, possibly even noise. If a model is too simple, it may miss important details (underfitting); if too complex, it might learn noise as if it were true, reducing its ability to make accurate predictions on new data (overfitting). Finding the right complexity helps the model generalize well to new, unseen data.