A manufacturing company has a large set of labeled historical sales data. The manufacturer would like to predict how many units of a particular part should be produced each quarter.
Which machine learning approach should be used to solve this problem?
A. Logistic regression
B. Random Cut Forest (RCF)
C. Principal component analysis (PCA)
D. Linear regression
D is the right answer.
D
RCF is:
1) unsupervised, should not require labeled historical data
2) is used for anomaly detection rather than numeric prediction