Which solutions will MOST improve the model generalization and reduce overfitting?

Explanations:

Shuffling the dataset can help in creating a better training process but does not directly address overfitting. It helps in reducing the risk of the model learning specific patterns from the order of the data but does not improve generalization on its own.

Decreasing the learning rate slows down the training process, which can sometimes help with convergence, but it does not inherently reduce overfitting. It may even prolong the training process, allowing the model to fit the training data more closely.

Increasing the number of layers in the network can lead to more complex models, which typically increases the risk of overfitting, as the model may learn more noise from the training data.

Adding L1 and L2 regularization helps prevent overfitting by adding a penalty to the loss function based on the size of the coefficients. This encourages the model to keep weights small and reduces complexity, thereby improving generalization.

Adding dropout involves randomly setting a fraction of the input units to 0 during training, which helps prevent co-adaptation of neurons. This effectively reduces overfitting by promoting redundancy in the network and improving generalization to unseen data.

Decreasing the number of layers in the network reduces model complexity, which can help mitigate overfitting by limiting the model’s capacity to memorize the training data, thus promoting better generalization.