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…

The correct answer is D. Linear regression. Predicting the number of units to produce is a continuous numerical output - a regression problem. Linear regression models a linear relationship between input features and a continuous target variable, and is the straightforward choice for this well-structured, labeled…

Modeling

Question

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?

Options

ALogistic regression
BRandom Cut Forest (RCF)
CPrincipal component analysis (PCA)
DLinear regression

How the community answered

(22 responses)

A
5% (1)
C
5% (1)
D
91% (20)

Explanation

Predicting the number of units to produce is a continuous numerical output - a regression problem. Linear regression models a linear relationship between input features and a continuous target variable, and is the straightforward choice for this well-structured, labeled historical dataset. Logistic regression (A) outputs a probability for a discrete class and is used for classification, not continuous prediction. Random Cut Forest (B) is an anomaly detection algorithm. PCA (C) is a dimensionality reduction technique used in preprocessing, not for making predictions.

Topics

#Regression#Supervised Learning#Algorithm Selection#Predictive Modeling

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