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…

The correct answer is B. Increase regularization. C. Increase dropout. F. Decrease feature combinations. Feature selection: consider using fewer feature combinations, decrease n-grams size, and decrease the number of numeric attribute bins. Increase the amount of regularization used https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html

Modeling

Question

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? (Choose three.)

Options

ADecrease regularization.
BIncrease regularization.
CIncrease dropout.
DDecrease dropout.
EIncrease feature combinations.
FDecrease feature combinations.

How the community answered

(32 responses)

A
16% (5)
B
75% (24)
D
6% (2)
E
3% (1)

Explanation

Feature selection: consider using fewer feature combinations, decrease n-grams size, and decrease the number of numeric attribute bins. Increase the amount of regularization used https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html

Topics

#Overfitting#Regularization#Dropout#Deep Learning Models

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