You work as a machine learning specialist for a publishing company. The company has labeled a historical dataset of publication sales data. Using this labeled data, you need to predict how many copies of a given publication should be produced each month.
Which machine learning algorithm type should you use to generate your predictions?

Answer options:

A.Linear regression
B.Principal component analysis
C.Random Cut Forest
D.Logistic regression

Answer: A
Option A is CORRECT. You are trying to solve a “how many” question, and your data is labeled. These two factors lead to the choice of linear regression as the best option from those given.
Option B is incorrect. The principal component analysis is used for dimensionality reduction, not for solving predictions of “how many” problems. Also, it is an unsupervised algorithm. We have labeled data. So we should use a supervised algorithm.
Option C is incorrect.The random cut forest is used primarily as an unsupervised algorithm for detecting anomalous data points within a data set. Since we have labeled data, we will use a supervised algorithm.
Option D is incorrect. Logistic regression is used to solve “yes/no” or binary predictions, not “how many” predictions.
Reference:
Please see the Amazon Machine Learning developer guide titled Regression Model Insights.
Please refer to the Amazon SageMaker developer guide titled Random Cut Forest (RCF) Algorithm.
Please refer to the Amazon SageMaker developer guide titled Principal Component Analysis (PCA) Algorithm.