Overfitting and Underfitting in Machine Learning

Overfitting and Underfitting With Machine Learning Algorithms overfitting Kaggle competitions are a particularly well-suited environment for studying overfitting since data sources are diverse, contestants use a wide range of model overfitting Overfitting can lead to misleading results and poor decision-making, while underfitting can result in models that fail to capture important patterns and

overfitting One way to manage overfitting and underfitting is to use statistical validation methods, such as cross-validation and regularization Cross-