COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Yes, the model performance always increases
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No, model performance always decreases
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The model performance may increase, but it can lead to overfitting
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The model performance may increase, but it can lead to underfitting
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Detailed explanation-1: -Having more data certainly increases the accuracy of your model, but there comes a stage where even adding infinite amounts of data cannot improve any more accuracy. This is what we called the natural noise of the data.
Detailed explanation-2: -In conclusion, adding more features expands the hypothesis space making the data more sparse and this might lead to overfitting problems.
Detailed explanation-3: -Too many features can lead to overfitting because it can increase model complexity. There is greater chance of redundancy in features and of features that are not at all related to prediction.
Detailed explanation-4: -Many enterprises assume that more training data will improve their AI, but dataset size is just one of many factors that influence accuracy. More training data improves AI performance up to a certain point but can compromise performance beyond it.