AWS Certified Cloud Practitioner
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Which ML model type meets these requirements?
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.
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RRitesh
A
Logistic regression model
B
Deep learning model built on principal components
C
K-nearest neighbors (k-NN) model
D
Neural network
Explanation:
Explanation
Logistic regression is a linear model that assigns weights to input features, making it highly interpretable. These weights can be viewed and adjusted by domain experts based on their knowledge — which directly satisfies the requirement of allowing employees to 'view and adjust the weights assigned to different variables.'
Why Logistic Regression is the Best Choice:
- Interpretability: Logistic regression provides clear, understandable coefficients (weights) for each input variable
- Adjustability: Domain experts can easily modify these weights based on their business knowledge
- Transparency: The model's decision-making process is transparent and explainable
Why Other Options Are Not Suitable:
- Deep learning models (B and D): These are often 'black boxes' — difficult to interpret and adjust weights meaningfully without extensive technical expertise
- K-NN (C): This is instance-based and doesn't use adjustable weights in the same way; it relies on distance metrics and nearest neighbors
Thus, Logistic regression is the most suitable model for this use case due to its transparency and tunability, allowing business users to incorporate their domain expertise directly into the model.
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