Which of these are reasons that it’s often unrealistic to expect an ML system to be 100% accurate?1 pointYou might not have enough dataData can be mislabeledData can be ambiguousAll of the above.
Question
Which of these are reasons that it’s often unrealistic to expect an ML system to be 100% accurate?1 pointYou might not have enough dataData can be mislabeledData can be ambiguousAll of the above.
Solution
All of the above.
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You might not have enough data: Machine Learning systems rely on large amounts of data to train and improve their accuracy. If there is not enough data, the system may not be able to learn effectively and make accurate predictions.
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Data can be mislabeled: If the data used to train the system is not labeled correctly, the system will learn from these mistakes and its accuracy will be affected.
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Data can be ambiguous: Machine Learning systems can struggle with ambiguous data. If the data is not clear, the system may not be able to make accurate predictions.
Therefore, it's often unrealistic to expect an ML system to be 100% accurate due to these reasons.
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