What is Model Complexity?

Model complexity refers is a measure of how well a model can capture the underlying patterns in the data. In the context of machine learning, model complexity is often associated with the number of parameters in a model and its ability to fit both the training data and generalize to new, unseen data.

There are two main aspects of model complexity:

1. Simple Models: Simple models have few parameters, making them less flexible therefore they struggle to capture the complexity of the underlying patterns in the data leading to underfitting, where the model performs poorly on the training data as well as on unseen data.
2. Complex Models: Complex models have a larger number of parameters, allowing them to represent more intricate relationships in the data. While complex models may perform well on the training data, model tends to overfitting.

Modelling complexity can be influenced by several factors:

1. Number of Features: The more attributes or features your model scrutinizes, the higher its complexity is likely to be. Too many features can potentially magnify noise and result in overfitting.
2. Model Algorithm: The nature of the algorithm used influences the complexity of the model. For instance, decision trees are considerably simpler than neural networks.
3. Hyperparameters: Settings such as the learning rate, number of hidden layers, and regularization parameters can influence the complexity of a machine learning model.

Model complexity leads to overfitting, which makes it harder to perform well on the unseen new data. In this article, we delve into the crucial challenges of model complexity and overfitting in machine learning.