A company wants to build a lead prioritization application for its employees to contact potential customers. The application must give employees the ability to view and adjust the weights assigned…

The correct answer is A. Logistic regression model. The company needs an ML model for a lead prioritization application where employees can view and adjust the weights assigned to different variables based on domain knowledge. Logistic regression is a linear model that assigns interpretable weights to input features, making it…

Submitted by yuki_2020· Mar 30, 2026Applications of AI and ML

Question

A company wants to build a lead prioritization application for its employees to contact potential customers. The application must give employees the ability to view and adjust the weights assigned to different variables in the model based on domain knowledge and expertise. Which ML model type meets these requirements?

Options

ALogistic regression model
BDeep learning model built on principal components
CK-nearest neighbors (k-NN) model
DNeural network

How the community answered

(45 responses)

A
64% (29)
B
22% (10)
C
9% (4)
D
4% (2)

Explanation

The company needs an ML model for a lead prioritization application where employees can view and adjust the weights assigned to different variables based on domain knowledge. Logistic regression is a linear model that assigns interpretable weights to input features, making it easy for users to understand and modify these weights. This interpretability and adjustability make it suitable for the requirements. Logistic regression is a supervised learning algorithm used for classification tasks. It is highly interpretable, as it assigns weights to each feature, allowing users to understand and adjust the importance of different variables based on domain expertise.

Topics

#logistic regression#model interpretability#feature weights#model selection

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