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The aim is to examine the causes of overfitting in machine learning models and propose strategies to mitigate it.

An Overview of Overfitting and its Solutions

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The aim is to examine the causes of overfitting in machine learning models and propose strategies to mitigate it.
Discussed the concept of overfitting and its impact on model generalization.
Introduced strategies like early-stopping, network-reduction, data-expansion, and regularization.
Early-stopping helps by halting training when performance optimization ceases.
Network-reduction effectively eliminates noise from training data.
Data-expansion allows fine-tuning of hyper-parameters using larger datasets.

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This overview discusses overfitting in supervised machine learning, exploring causes and solutions.

Ying Xue (2019) studied this question.

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