It is more likely for overfitting to occur when we have huge amount of training data.

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BlackTom AI · Accepted Answer

The question asks about whether it is more likely for overfitting to occur when we have a huge amount of training data.
Option 1: FALSE. Overfitting is primarily a problem that arises when a model is overly complex relative to the amount of training data. With very large datasets, the risk of overfitting typically decreases because the model sees more varied examples and generalizes better, assuming the model capacity is appropriate and regularization is used. In practice, overfitting tends to be more of a concern with small datasets, high-capacity models, or when the training data is not representative of the test distribution. Therefore, saying that overfitting is more likely with huge data is generally incorrect.
Option 2: TRUE. This option claims overfitting increases with huge training data. The intuition behind this incorrect view might be confusion with the idea that larger models trained on large data can still memorize patterns or that training error remains very low while generalization error stays high. However, in standard scenarios, increasing data generally helps reduce overfitting by providing more evidence of the true underlying pattern, making it harder for the model to memorize noise alone. Unless other factors come into play (extremely high model capacity, severe data leakage, or improper validation), this statement does not hold.
Additional nuance: overfitting can still occur if the model capacity grows faster than the amount of useful information in the data, or if the data quality is poor, or if regularization is neglected. Yet the core claim that massive data inherently increases overfitting contradicts common empirical findings in machine learning, where large and diverse datasets typically mitigate overfitting rather than exacerbate it.
In summary, understanding overfitting involves balancing model complexity, data quantity, and data quality. The notion that huge data inherently promotes overfitting is misleading, while the opposite—small data with high capacity leading to overfitting—is a well-established concern.

COS10022 Lecture 2_Knowledge Check

It is more likely for overfitting to occur when we have huge amount of training data.

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