You work for a consumer electronics company as a machine learning specialist. Over time your company has built up a large set of labeled historical consumer electronic device sales data. You have been given the task of predicting how many memory components should be produced each quarter to satisfy the demand for your consumer electronic products. Which algorithm should you choose to get the best performing model to solve this prediction problem?

Answer options:

A.Linear regression
B.Latent Dirichlet Allocation (LDA)
C.Sequence-to-Sequence
D.Logistic regression

Correct Answer: A
Option A is correct. When you solve a continuous number for your prediction (how many), you use linear regression. If you are solving for a binary prediction (yes/no), you use logistic regression.
Option B is incorrect. Latent Dirichlet Allocation (LDA) is not used for the prediction of continuous values. LDA is an approach used as an unsupervised learning algorithm that attempts to describe a set of observations as a mixture of distinct categories. Using LDA, you discover a user-specified number of topics shared by documents within a text corpus.
Option C is incorrect. This option is incorrect because the Sequence-to-Sequence algorithm is primarily used as a supervised algorithm for language translation, text summarization, and speech-to-text. You would not use a Sequence-to-Sequence algorithm to solve a regression problem.
Option D is incorrect. When you solve a continuous number for your prediction (how many), you use linear regression. If you are solving for a binary prediction (yes/no), you use logistic regression.
References:
Please see the Amazon SageMaker developer guide titled Latent Dirichlet Allocation (LDA) Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/lda.html),
The Amazon SageMaker developer guide titled Linear Learner Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/linear-learner.html),
The Amazon SageMaker developer guide titled Sequence-to-Sequence Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/seq-2-seq.html),
The Amazon Amazon Machine Learning developer guide titled Regression Model Insights (https://docs.aws.amazon.com/machine-learning/latest/dg/regression-model-insights.html)