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Answer: Build an AutoML tabular regression model. Configure the model to generate explanations when it makes predictions.
Since interpretability is crucial for your churn prediction model to inform marketing campaigns, using an interpretable model is key. An AutoML tabular regression model can be configured to generate explanations for each prediction, helping you understand why certain customers are at risk of churning. This allows the marketing team to develop targeted campaigns based on the factors leading to churn. Although the problem may seem classification-based, the term 'probability of churn' can be addressed effectively through regression models like AutoML which provide explainable results. Hence, the correct answer is B.
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You are a data scientist working for a retail company. Your goal is to build a predictive model to estimate the probability of customer churn, i.e., the likelihood that a customer will stop purchasing from the company. The results must be interpretable so that the insights can guide marketing teams to develop targeted campaigns aimed at retaining at-risk customers. What should you do to achieve this?
A
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.
B
Build an AutoML tabular regression model. Configure the model to generate explanations when it makes predictions.
C
Build a custom TensorFlow neural network by using Vertex AI custom training. Configure the model to generate explanations when it makes predictions.
D
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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