Which of the following methods should the Specialist consider using to correct this?

A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.

Which of the following methods should the Specialist consider using to correct this? (Select THREE.)
A . Decrease regularization.
B . Increase regularization.
C . Increase dropout.
D . Decrease dropout.
E . Increase feature combinations.
F . Decrease feature combinations.

View Answer

Answer: B, C, F

Explanation:

The problem of poor performance on the test data is a sign of overfitting, which means the model has learned the training data too well and failed to generalize to new and unseen data. To correct this, the Machine Learning Specialist should consider using methods that reduce the complexity of the model and increase its ability to generalize.

Some of these methods are:

Increase regularization: Regularization is a technique that adds a penalty term to the loss function of the model, which reduces the magnitude of the model weights and prevents overfitting. There are different types of regularization, such as L1, L2, and elastic net, that apply different penalties to the weights1.

Increase dropout: Dropout is a technique that randomly drops out some units or connections in the neural network during training, which reduces the co-dependency of the units and prevents overfitting. Dropout can be applied to different layers of the network, and the dropout rate can be tuned to control the amount of dropout2.

Decrease feature combinations: Feature combinations are the interactions between different input

features that can be used to create new features for the model. However, too many feature

combinations can increase the complexity of the model and cause overfitting. Therefore, the

Specialist should decrease the number of feature combinations and select only the most relevant and

informative ones for the model3.

Reference:

1: Regularization for Deep Learning – Amazon SageMaker

2: Dropout – Amazon SageMaker

3: Feature Engineering – Amazon SageMaker