Fill in the blanks A series of predicted values, pred_mpg , have been calculated from the fitted model, model.Choose the appropriate metric to assess the predictive performance. Select the code to return the output library (Metrics) model <- Lm(mpg ~ ., data = train) pred_mpg <- predict(model, newdata = test) |(actual = test$mpg, predicted = pred_mpg) rmse accuracy precision auc Expected Output [1] 4.418243
Fill in the blanks A series of predicted values, pred_mpg , have been calculated from the fitted model, model.Choose the appropriate metric to assess the predictive performance. Select the code to return the output library (Metrics) model <- Lm(mpg ~ ., data = train) pred_mpg <- predict(model, newdata = test) |(actual = test$mpg, predicted = pred_mpg) rmse accuracy precision auc Expected Output [1] 4.418243
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
Related questions
Question
Fill in the blank
![Fill in the blanks
A series of predicted values, pred_mpg , have been calculated from the fitted model,
model.Choose the appropriate metric to assess the predictive performance.
Select the code to return the output
library (Metrics)
model <- Lm (mpg - ., data = train)
pred_mpg <- predict (model, newdata = test)
? (actual = test$mpg, predicted = pred_mpg)
rmse
accuracy precision
auc
Expected Output
[1] 4.418243](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F84e7da27-c6ee-4c6c-9311-b4850237eafe%2F355a8df0-c36a-46cc-a973-f90d6186bdd0%2Fbn81q8m_processed.png&w=3840&q=75)
Transcribed Image Text:Fill in the blanks
A series of predicted values, pred_mpg , have been calculated from the fitted model,
model.Choose the appropriate metric to assess the predictive performance.
Select the code to return the output
library (Metrics)
model <- Lm (mpg - ., data = train)
pred_mpg <- predict (model, newdata = test)
? (actual = test$mpg, predicted = pred_mpg)
rmse
accuracy precision
auc
Expected Output
[1] 4.418243
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