A manufacturer has a large labeled history of sales and wants to forecast how many units of a particular part to produce each quarter. Which machine learning approach is appropriate for predicting a continuous numeric quantity?

Linear regression is appropriate because it is used to model the relationship between a dependent variable (the number of units to produce) and one or more independent variables (historical sales data, quarter). Since the number of units is a continuous numeric quantity, linear regression, which predicts continuous values, is the correct choice. Logistic regression is incorrect because it is used for classification tasks, predicting a categorical outcome. Random Cut Forest (RCF) is incorrect as it is an unsupervised algorithm primarily used for anomaly detection. Principal Component Analysis (PCA) is incorrect because it is a dimensionality reduction technique, not a predictive model.