Answer-first summary for fast verification
Answer: Apply the exponentiation function to the predictions before calculating the RMSE.
The correct answer is **C**. Since the model predicts log(price), the predictions are on a log scale. To compare these predictions directly with the original price values, we must first convert the predictions back to the original scale using the exponentiation function (e.g., `np.exp` in Python). Only then should we calculate the RMSE to assess the model's accuracy in terms of actual prices. This approach ensures that the RMSE reflects the model's performance in the context of the original price scale, making it meaningful for practical interpretation. - **Option A** is incorrect because it suggests calculating the MSE of log-transformed predictions, which doesn't address the need to revert predictions to the original scale. - **Option B** is incorrect as applying the logarithm to already log-transformed predictions doesn't convert them back to the original scale. - **Option D** and **Option E** are incorrect because they involve manipulating the RMSE value itself rather than the predictions, which doesn't achieve the desired comparability with the original price scale.
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A data scientist has developed a linear regression model using log(price) as the target variable. After making predictions, both the predicted and actual values are stored in a Spark DataFrame named preds_df. The data scientist evaluates the model using the following code: regression_evaluator.setMetricName("rmse").evaluate(preds_df). What modification is necessary to the RMSE evaluation approach to ensure it's comparable with the original price scale? Choose the single best answer.
A
Calculate the MSE of the log-transformed predictions to obtain the RMSE.
B
Apply the logarithm to the predictions prior to calculating the RMSE.
C
Apply the exponentiation function to the predictions before calculating the RMSE.
D
Take the exponent of the computed RMSE value.
E
Compute the logarithm of the derived RMSE value.