Linear Regression Algorithm

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)

For this prediction problem, the most appropriate algorithm would be linear regression (option A). Linear regression is a supervised learning algorithm used for regression problems where the target variable is continuous. In this case, the target variable is the number of memory components to be produced each quarter, which is a continuous variable.

Linear regression models aim to establish a relationship between the independent variable(s) and the dependent variable by fitting a linear equation to the data. The model predicts the dependent variable as a linear function of the independent variable(s). The equation of the line can be represented as:

y = b0 + b1x1 + b2x2 + ... + bn*xn

where y is the dependent variable, x1, x2, ..., xn are the independent variables, and b0, b1, b2, ..., bn are the coefficients that represent the slope of the line.

The goal of linear regression is to estimate the values of the coefficients b0, b1, b2, ..., bn that minimize the difference between the predicted values and the actual values of the dependent variable. This difference is called the residual, and the technique used to minimize it is called the method of least squares.

In the case of the consumer electronics company, the historical sales data can be used as the independent variable, and the number of memory components sold each quarter can be used as the dependent variable. By fitting a linear regression model to this data, the company can predict the number of memory components to be produced each quarter based on the historical sales data.

Latent Dirichlet Allocation (LDA) (option B) is an unsupervised learning algorithm used for topic modeling. It is not suitable for this prediction problem because it is not a regression algorithm and does not predict continuous variables.

Sequence-to-Sequence (option C) is a deep learning architecture used for sequence prediction problems. It is not suitable for this problem because it is a complex architecture that is generally used for more complex problems that involve sequences of inputs and outputs.

Logistic regression (option D) is a supervised learning algorithm used for classification problems where the target variable is categorical. It is not suitable for this problem because the target variable (the number of memory components) is continuous, not categorical.

Therefore, the most appropriate algorithm for this prediction problem is linear regression.