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Data leakage in machine learning occurs when a model uses information during training that wouldn't be available at the time of prediction. Learn about the risks of data leakage in machine learning models and discover prevention strategies to ensure their accuracy and reliability. In statistics and machine learning, leakage (also known as data leakage or target leakage) is the use of information in the model training process which would not be expected to be available at prediction time, causing the predictive scores (metrics) to overestimate the model's utility when run in a production environment
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[1] leakage is often subtle and indirect, making it hard to detect and. Abstract with the increasing reliance on machine learning (ml) across diverse disciplines, ml code has been subject to a number of issues that impact its quality, such as lack of documentation, algorithmic biases, overfitting, lack of reproducibility, inadequate data preprocessing, and potential for data leakage, all of which can significantly affect the performance and reliability of ml. Conclusion data leakage is a critical issue that can compromise the validity of machine learning models and predictive analytics
By understanding its causes and implementing robust prevention strategies, data scientists and analysts can build more reliable and accurate models.
Data leakage is one of the most common pitfalls in machine learning that can lead to deceptively high performance during model training and…
