Topic 1 Question 113
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?
Logistic regression model
Deep learning model built on principal components
K-nearest neighbors (k-NN) model
Neural network
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- 正解だと思う選択肢: A
A: Logistic regression model
Explanation: A logistic regression model is interpretable and allows direct adjustment of the weights assigned to different variables (features). This aligns with the requirement for employees to view and modify the weights based on their domain knowledge and expertise. Logistic regression provides a clear relationship between input features and output predictions, making it ideal for use cases that demand transparency and control.
👍 2Moon2024/12/31 - 正解だと思う選択肢: A
Logistic regression models are interpretable and allow the user to view and adjust the weights assigned to different variables (features). These weights determine the contribution of each feature to the final prediction, and they can be modified based on domain knowledge or expertise.
This characteristic makes logistic regression a suitable choice for the lead prioritization application, as employees can easily understand and fine-tune the model to align with their specific business requirements.
👍 1ap64912024/12/27 - 正解だと思う選択肢: A
A logistic regression model allows for clear, adjustable weights based on domain knowledge, making it the best choice for a lead prioritization application where employees can modify model parameters easily.
👍 1aws_Tamilan2024/12/27
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