Topic 1 Question 195
You work for a retail company. You have been asked to develop a model to predict whether a customer will purchase a product on a given day. Your team has processed the company’s sales data, and created a table with the following rows: • Customer_id • Product_id • Date • Days_since_last_purchase (measured in days) • Average_purchase_frequency (measured in 1/days) • Purchase (binary class, if customer purchased product on the Date)
You need to interpret your model’s results for each individual prediction. What should you do?
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.
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.
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
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.
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- 正解だと思う選択肢: B
Individual prediction interpretability: Feature attributions specifically address the need to understand how features contribute to individual predictions, providing fine-grained insights. Vertex AI integration: Vertex AI offers seamless integration of feature attributions with AutoML models, simplifying the process. Model flexibility: AutoML can explore various model architectures, potentially finding the most suitable one for this task, while still providing interpretability.
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