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Answer: Create a Vertex AI tabular dataset. Train an AutoML model to predict customer purchases. Deploy the model to a Vertex AI endpoint and enable feature attributions. Use the 'explain' method to get feature attribution values for each individual prediction.
The correct answer is B. By using Vertex AI's AutoML model and enabling feature attributions, you get detailed insights into how each feature contributes to an individual prediction. This method provides a clear, instance-specific explanation of why the model made a certain prediction, which is essential for model interpretability. Option A, although it involves inspecting partition rules of trees, can be cumbersome and less intuitive. Option C provides coefficient values for feature importance but does not directly explain individual predictions. Option D involves L1 regularization, which helps identify non-informative features but does not directly explain predictions at the instance level.
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You work for a retail company and are tasked with developing a predictive model to determine whether a customer will purchase a product on a given day. To aid this, your team has processed sales data and created a table with the following columns: Customer_id, Product_id, Date, Days_since_last_purchase (measured in days), Average_purchase_frequency (measured in 1/days), and Purchase (binary class indicating if the customer purchased the product on the given date). It is essential to interpret your model’s predictions for each individual instance to provide actionable insights. What should you do?
A
Create a BigQuery table. Use BigQuery ML to build a boosted tree classifier. Inspect the partition rules of the trees to understand how each prediction flows through the trees.
B
Create a Vertex AI tabular dataset. Train an AutoML model to predict customer purchases. Deploy the model to a Vertex AI endpoint and enable feature attributions. Use the 'explain' method to get feature attribution values for each individual prediction.
C
Create a BigQuery table. Use BigQuery ML to build a logistic regression classification model. Use the values of the coefficients of the model to interpret the feature importance, with higher values corresponding to more importance.
D
Create a Vertex AI tabular dataset. Train an AutoML model to predict customer purchases. Deploy the model to a Vertex AI endpoint. At each prediction, enable L1 regularization to detect non-informative features.