Topic 1 Question 264
You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be used to develop marketing campaigns that target at-risk customers. What should you do?
Build a random forest regression model in a Vertex AI Workbench notebook instance. Configure the model to generate feature importances after the model is trained.
Build an AutoML tabular regression model. Configure the model to generate explanations when it makes predictions.
Build a custom TensorFlow neural network by using Vertex AI custom training. Configure the model to generate explanations when it makes predictions.
Build a random forest classification model in a Vertex AI Workbench notebook instance. Configure the model to generate feature importances after the model is trained.
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- 正解だと思う選択肢: D
Option A: Regression, not classification, is used for random forest model, which is not appropriate for predicting probabilities. Option B: While AutoML tabular can generate model explanations, random forests inherently provide more granular insights into feature importance. Option C: Neural networks can be less interpretable than tree-based models, and generating explanations for them often requires additional techniques and libraries.
👍 1pikachu0072024/01/13
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