You are a machine learning specialist at a pharmaceutical drug manufacturer. Your team has the task of building a deep learning model to be used for drug discovery by combining data from various sources. Your team will use the deep learning model to predict novel candidate biomolecules for new disease targets such as COVID 19.You and your team have created a deep learning neural network disease target prediction model that performs well on the training data. However, it performs poorly on your test data.
Which of the methods listed should your team use to correct your testing problem? (SELECT THREE)

Answer options:

A.Increase dropout
B.Decrease regularization
C.Increase regularization
D.Decrease dropout
E.Decrease feature combinations
F.Increase feature combinations

Answers: A, C and E
Option A is CORRECT. When your model performs well in training but poorly in testing, it quite often means the model is overfitted or not generalized enough. Increasing dropout, or the probability of a node to be turned off, helps generalization.
Option B is incorrect. You need to increase regularization to avoid overfitting, a common problem when your training data is “memorized” by your neural network. Decreasing regularization is used when your model is underfitting.
Option C is CORRECT. You need to increase regularization to avoid overfitting, a common problem when your training data is “memorized” by your neural network. Increasing regularization helps generalization.
Option D is incorrect. Decreasing dropout will not help generalization. It will have the opposite effect.
Option E is CORRECT. Decreasing feature combinations and combining the strength of multiple complementary features to yield more powerful features (reducing the number of features) will help with generalization.
Option F is incorrect. Increasing feature combinations is used when your model is underfitting. Decreasing feature combinations, combining the strength of multiple complementary features to yield more powerful features (reducing the number of features) will help with generalization.
Reference:
Please see the Amazon Machine Learning developer guide titled Model Fit: Underfitting vs. Overfitting.
Please refer to the Machine Learning Mastery article titled A Gentle Introduction to Dropout for Regularizing Deep Neural Networks.