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Data Poisoning is the name given to the situation when the ML model used to train the data is misleading the model in detecting a data poisoning event and misclassifying innocent data. It leads to the prediction of a false positive for a valid input and also false negatives for an innocent input. Poisoning is known as a problem when the model produces incorrect predictions even if all training data is good. Extra resources This is not because the model is wrong or faulty. This happens because the model has learned that the pattern of a set of data points with high probability of
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Data poisoning is the threat of introducing unwanted changes into a dataset, leading to a decline in the performance of a machine learning (ML) model. The threat of data poisoning has been highlighted by recent real-world events such as the Equifax data breach, where credit card and identity theft data was compromised due to inadequate data protection measures in the company’s IT infrastructure. Additionally, data poisoning is a frequent occurrence in the healthcare sector, as malware and other viruses are used to breach and manipulate patient data
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