Which of the following code blocks can be used to compute the root mean-squared-error of the model according to the data in preds_df and assign it to the rmse variable?

A data scientist has developed a linear regression model using Spark ML and computed the predictions in a Spark DataFrame preds_df with the following schema: prediction DOUBLE actual DOUBLE

Which of the following code blocks can be used to compute the root mean-squared-error of the model according to the data in preds_df and assign it to the rmse variable?

A)

B)

C)

D)

E)

A . Option A
B . Option B
C . Option C
D . Option D
E . Option E

View Answer

Answer: C

Explanation:

The code block to compute the root mean-squared error (RMSE) for a linear regression model in Spark ML should use the RegressionEvaluator class with metricName set to "rmse". Given the schema of preds_df with columns prediction and actual, the correct evaluator setup will specify predictionCol="prediction" and labelCol="actual". Thus, the appropriate code block (Option C in your list) that uses RegressionEvaluator to compute the RMSE is the correct choice. This setup correctly measures the performance of the regression model using the predictions and actual outcomes from the DataFrame.

Reference: Spark ML documentation (Using RegressionEvaluator to Compute RMSE).