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Overfitting occurs when a machine learning model performs well on training data but poorly on unseen data, often due to excessive complexity or insufficient generalization. Dropout is a regularization technique that helps mitigate overfitting by randomly "dropping out" or deactivating a fraction of neurons during training. This prevents the model from becoming overly reliant on specific neurons and promotes robustness in learning. For example, in deep learning models, a dropout rate of 0.5 ensures that 50% of neurons are deactivated in each forward pass, encouraging diverse feature representations. By leveraging dropout, neural networks become less prone to memorizing training data and improve generalization on test datasets. Why Other Options Are Incorrect :
Consent is said to be free when it is not caused by________________
The main principles which underline the law of evidence are
Chief Judicial Magistrate may pass a
The repealment of the Rajasthan Premises (Control of Rent and Eviction) Act, 1950 ("Act of 1950") by Section 32(1) of The Rajasthan Rent Control Act, 20...
For the purpose of examination of a person, a court can issue commission to -
As per Companies Act, 2013, an auditor of a company cannot render which of the following service?
A corporate debtor shall be dissolved under the IBC as per section 54 by the order of the ___________________
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Which of the following statement is true?
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