Discover how Vertex Explainable AI’s sampled Shapley method reveals the most predictive customer attributes for subscription renewal predictions, empowering data-driven decisions in the magazine distribution industry.
Table of Contents
Question
You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company’s historical data as your training set, you created a TensorFlow model and deployed it to Vertex AI. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?
A. Stream prediction results to BigQuery. Use BigQuery’s CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.
B. Use Vertex Explainable AI. Submit each prediction request with the explain’ keyword to retrieve feature attributions using the sampled Shapley method.
C. Use Vertex AI Workbench user-managed notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.
D. Use the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model.
Answer
B. Use Vertex Explainable AI. Submit each prediction request with the explain’ keyword to retrieve feature attributions using the sampled Shapley method.
Explanation
The correct approach to determine which customer attribute has the most predictive power for each prediction served by the deployed TensorFlow model on Vertex AI is to use Vertex Explainable AI (Option B).
By submitting each prediction request with the ‘explain’ keyword, Vertex Explainable AI retrieves feature attributions using the sampled Shapley method. The Shapley value is a game-theoretic approach that assigns importance scores to each feature, indicating its contribution to the model’s prediction for a specific instance.
The sampled Shapley method approximates the Shapley values by considering subsets of features, providing a computationally efficient way to estimate feature importance. This method is particularly useful when dealing with complex models and large feature spaces.
Vertex Explainable AI enables you to gain insights into the model’s decision-making process by identifying the most influential features for each prediction. By analyzing the feature attributions, you can determine which customer attributes have the greatest impact on the predicted likelihood of subscription renewal.
This information can be valuable for targeted marketing strategies, personalized offers, and understanding customer behavior. By focusing on the most predictive attributes, you can optimize your efforts to retain customers and improve subscription renewal rates.
In summary, leveraging Vertex Explainable AI with the sampled Shapley method is the most effective approach to identify the customer attributes with the highest predictive power for each prediction served by your deployed TensorFlow model on Vertex AI.
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