試験Databricks-Machine-Learning-Associate トピック3 問題20 スレッド
Databricks Databricks-Machine-Learning-Associateのリアル試験問題集
問題 #: 20
トピック #: 3
問題 #: 20
トピック #: 3
A data scientist has created a linear regression model that uses log(price) as a label variable. Using this model, they have performed inference and the predictions and actual label values are in Spark DataFrame preds_df.
They are using the following code block to evaluate the model:
regression_evaluator.setMetricName("rmse").evaluate(preds_df)
Which of the following changes should the data scientist make to evaluate the RMSE in a way that is comparable with price?
They are using the following code block to evaluate the model:
regression_evaluator.setMetricName("rmse").evaluate(preds_df)
Which of the following changes should the data scientist make to evaluate the RMSE in a way that is comparable with price?
おすすめの解答：D 解答を投票する
When evaluating the RMSE for a model that predicts log-transformed prices, the predictions need to be transformed back to the original scale to obtain an RMSE that is comparable with the actual price values. This is done by exponentiating the predictions before computing the RMSE. The RMSE should be computed on the same scale as the original data to provide a meaningful measure of error.
Reference:
Databricks documentation on regression evaluation: Regression Evaluation
Reference:
Databricks documentation on regression evaluation: Regression Evaluation
Yamaguchi 2025-07-28 07:12:28
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