Answer Posted / Bhupesh Kumar Gautam

To prevent overfitting, several strategies can be employed. Firstly, validating the model using a separate test dataset that was not used during training. Secondly, cross-validation techniques like k-fold or leave-one-out methods are useful for assessing the generalization performance of the model. Thirdly, regularization techniques such as L1 and L2 regularization can be applied to reduce the complexity of the model. Lastly, increasing the amount of training data can help in reducing overfitting.

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