Regularization Techniques QUIZ (MCQ QUESTIONS AND ANSWERS)

It reduces the model's complexity.

It increases the size of the training dataset.

It improves the model's generalization ability.

It makes the model deeper.

It makes the model more complex.

It reduces training time.

It provides a more effective defense against overfitting.

It increases the learning rate.

Dropout regularization

L1 regularization

Weight decay

Data augmentation

The absolute value of the weights

The square of the weights

The logarithm of the weights

The exponential of the weights

Weight decay

L1 regularization

Dropout regularization

Data augmentation

It reduces the number of parameters in the model.

It makes the model more complex.

It normalizes activations, making training more stable.

It increases the learning rate.

L1 regularization

L2 regularization

Dropout regularization

Data augmentation

To reduce training time

To increase the learning rate

To prevent overfitting by monitoring the validation loss

To add noise to the input data

L1 regularization

Dropout regularization

Batch normalization

Early stopping

It adds a penalty term based on the absolute values of the weights.

It increases the learning rate.

It decreases the batch size.

It adds random noise to the input data.

L1 regularization is more computationally efficient.

L1 regularization is less sensitive to the choice of hyperparameters.

L1 regularization encourages sparsity in the model weights.

L1 regularization is less effective in preventing overfitting.

L1 regularization

L2 regularization

Dropout regularization

Batch normalization

To increase the model's complexity

To reduce the number of neurons in each layer

To prevent overfitting by introducing randomness during training

To make the model deterministic

When the training loss is decreasing rapidly

When the model has a small number of parameters

When the dataset is very large

When the model is underfitting

Weight decay

L1 regularization

L2 regularization

Early stopping

To reduce overfitting by randomly dropping neurons during training

To increase the number of neurons in each layer

To speed up the training process

To make the model deeper

Weight decay

Data augmentation

Dropout regularization

Early stopping

To add more layers to the network

To increase the learning rate

To randomly drop a fraction of neurons during training

To increase the batch size

Weight decay

L2 regularization

Parameter sharing

Early stopping

It adds noise to the input data.

It normalizes the activations of each layer, making training more stable.

It increases the learning rate during training.

It applies L1 and L2 regularization to the weights.

L1 regularization

L2 regularization

Dropout regularization

Ensemble learning

0

0.5

1

2

L1 regularization

L2 regularization

Gradient clipping

Early stopping

To increase the weights of important features

To decrease the weights of important features

To encourage sparsity in the weights

To shrink the weights toward zero

It stops training when the loss on the validation set starts increasing.

It increases the learning rate during training.

It adds noise to the input data.

It increases the number of hidden layers in the network.

Dropout regularization

Early stopping

Noise injection

L2 regularization

To reduce the size of the training dataset

To increase the complexity of the model

To create additional training examples by applying transformations to the data

To decrease the learning rate during training

Dropout regularization

Elastic Net regularization

Batch normalization

Gradient clipping

The square of the weights

The absolute value of the weights

The exponential of the weights

The logarithm of the weights

L1 regularization

L2 regularization

Dropout regularization

Batch normalization