Which of the following techniques is primarily used to address overfitting in machine learning models?

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 :

1. Increasing the model's complexity : Adding complexity exacerbates overfitting by enabling the model to memorize the training data rather than generalize from it.
2. Using larger datasets : While larger datasets reduce overfitting, they may not always be feasible, and they don't directly address model regularization like dropout does.
3. Applying one-hot encoding : One-hot encoding is a preprocessing step for categorical variables, unrelated to overfitting or model regularization.
4. Reducing the number of hidden layers : This can oversimplify the model, leading to underfitting instead of solving the overfitting problem.