Model Complexity
Model complexity refers to the level of sophistication of a statistical or machine learning model in capturing the underlying patterns in the data. A model with high complexity can capture intricate relationships between the input features and the target variable, but it may also overfit the training data and perform poorly on new, unseen data. On the other hand, a model with low complexity may not be able to capture all the relevant information in the data, leading to underfitting. The choice of model complexity depends on the trade-off between bias and variance, and can be determined through techniques such as cross-validation, regularization, and model selection.
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