Calculate test set RMSE by hand
Now that you have predictions on the test set, you can use these predictions to calculate an error metric (in this case RMSE) on the test set and see how the model performs out-of-sample, rather than in-sample as you did in the first exercise. You first do this by calculating the errors between the predicted diamond prices and the actual diamond prices by subtracting the predictions from the actual values.
Once you have an error vector, calculating RMSE is as simple as squaring it, taking the mean, then taking the square root:
sqrt(mean(error^2))
本练习是课程的一部分
Machine Learning with caret in R
练习说明
test, model, and p are loaded in your workspace.
- Calculate the error between the predictions on the test set and the actual diamond prices in the test set. Call this
error. - Calculate RMSE using this error vector, just printing the result to the console.
交互式实操练习
通过完成这段示例代码来试试这个练习。
# Compute errors: error
# Calculate RMSE