You are a machine learning specialist working for a polling company where you have been given the assignment of creating a machine learning model that predicts voter turnout in various voting districts across the voting population. You have created a deep learning neural network model for your predictions. The model performs well on the training data, 99% accuracy, but it performs poorly on the test data, 70% accuracy. Which techniques can you leverage to address this overfitting situation with your deep learning model? (Select TWO)

Answer options:

A.Increase feature dimensionality.
B.Lower the dropout rate.
C.Increase the dropout rate.
D.Increase L1 and/or L2 regularization amount.
E.Decrease L1 and/or L2 regularization amount.

Correct Answers: C and D
Option A is incorrect. Your model is overfitting. You should decrease the feature combinations (dimensionality) using a technique such as Principal Component Analysis (PCA) which will help with overfitting. Increasing feature combinations, or increasing the feature dimensionality, would make the model have the opposite effect.
Option B is incorrect. With an overfitting model, you need to increase your dropout rate to help the model reach generalization. Decreasing dropout will have the opposite effect.
Option C is correct. Increasing the dropout rate in your deep learning model is a proven technique to address the issue of training data overfitting.
Option D is correct. Increasing regularization through L1 regularization, L2 regularization, or dropout helps lower the complexity of the model to help address overfitting.
Option E is incorrect. Decreasing regularization will increase the complexity of your model and will not help address overfitting.
References:
Please see the Machine Learning Mastery article titled A Gentle Introduction to Dropout for Regularizing Deep Neural Networks (https://machinelearningmastery.com/dropout-for-regularizing-deep-neural-networks/),
The Towards Data Science article titled Regularization in Deep Learning — L1, L2, and Dropout (https://towardsdatascience.com/regularization-in-deep-learning-l1-l2-and-dropout-377e75acc036),
The Amazon SageMaker developer guide titled Principal Component Analysis (PCA) Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/pca.html)