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In the realm of data science and machine learning, data leakage is a term that denotes a critical problem that can severely impact the performance and credibility of predictive models Detecting this problem can be quite challenging, this is why prevention of data leakage becomes an urgent task in the field of machine learning. Despite its significance, data leakage is often misunderstood or overlooked, leading to erroneous conclusions and unreliable outcomes

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This article delves into what data leakage is. Data leakage results in lower accuracy of the model compared to the estimation during tests Data leakage in machine learning occurs when a model uses information during training that wouldn't be available at the time of prediction.

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

[1] leakage is often subtle and indirect, making it hard to detect and. Data leakage is one of the most common pitfalls in machine learning that can lead to deceptively high performance during model training and… Feature leakage probably the most famous data leakage source (and the easiest to spot, as well) Feature leakage happens when we have features that are directly related to the target often being a result of the target event

Normally, feature leakage happens because the feature value is updated in a point in time after the target event. Data leakage is a critical issue in machine learning that can significantly compromise the fairness, generalizability, and security of models By understanding the various types of data leakage and implementing robust prevention strategies, data scientists can ensure the integrity and reliability of their machine learning models.