You work as a machine learning specialist at a government agency that creates an image recognition program to help detect missing persons by analyzing surveillance videos. You have built and are now training a deep learning model for your image classification. You see that it is overfitting the training data during your model training: your training accuracy is 99%, and your testing accuracy is 75%. Why is your model overfitting the training data, and how can you address the issue?

Answer options:

A.Optimization stopped before model training bounced out of a local minimum. Address the issue by increasing the epoch number.
B.The mini-batch size is too low. Address the issue by increasing the mini-batch size.
C.The model is not generalized. Address the issue by increasing the dropout rate at the flatten layer.
D.Optimization is trapped at a local minimum during training. Address the issue by increasing the learning rate.

Correct Answer: C
Option A is incorrect. Increasing the epoch number will cause your model to train longer. But this alone won’t allow your training to reach generalization.
Option B is incorrect. Increasing the mini-batch size results in models with poor generalization, therefore not allowing your training to reach generalization.
Option C is correct. Increasing the dropout rate in your deep learning model is proven to address the issue of overfitting. See the article “Dropout Regularization in Deep Learning Models With Keras” in the reference section.
Option D is incorrect. Increasing the learning rate alone won’t bring your model to generalization. A learning rate that is too large will result in an unstable network. See the article “Understand the Impact of Learning Rate on Neural Network Performance” in the reference section.
References:
Please see the Machine Learning Mastery article titled Dropout Regularization in Deep Learning Models With Keras (https://machinelearningmastery.com/dropout-regularization-deep-learning-models-keras/),
The Machine Learning Mastery article titled Understand the Impact of Learning Rate on Neural Network Performance (https://machinelearningmastery.com/understand-the-dynamics-of-learning-rate-on-deep-learning-neural-networks/),
The AWS Glue developer guide titled Populating the AWS Glue Data Catalog (https://docs.aws.amazon.com/glue/latest/dg/populate-data-catalog.html),
The Amazon SageMaker developer guide titled Object2Vec Hyperparameters (particularly the descriptions of dropout and learning_rate) (https://docs.aws.amazon.com/sagemaker/latest/dg/object2vec-hyperparameters.html)