Try an 80/20 split
Now that your dataset is randomly ordered, you can split the first 80% of it into a training set, and the last 20% into a test set. You can do this by choosing a split point approximately 80% of the way through your data:
split <- round(nrow(mydata) * 0.80)
You can then use this point to break off the first 80% of the dataset as a training set:
mydata[1:split, ]
And then you can use that same point to determine the test set:
mydata[(split + 1):nrow(mydata), ]
This is a part of the course
“Machine Learning with caret in R”
Exercise instructions
- Choose a row index to split on so that the split point is approximately 80% of the way through the
diamondsdataset. Call this indexsplit. - Create a training set called
trainusing that index. - Create a test set called
testusing that index.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Determine row to split on: split
# Create train
# Create test
This exercise is part of the course
Machine Learning with caret in R
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
In the first chapter of this course, you'll fit regression models with <code>train()</code> and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE).
Exercise 1: Welcome to the courseExercise 2: In-sample RMSE for linear regressionExercise 3: In-sample RMSE for linear regression on diamondsExercise 4: Out-of-sample error measuresExercise 5: Out-of-sample RMSE for linear regressionExercise 6: Randomly order the data frameExercise 7: Try an 80/20 splitExercise 8: Predict on test setExercise 9: Calculate test set RMSE by handExercise 10: Comparing out-of-sample RMSE to in-sample RMSEExercise 11: Cross-validationExercise 12: Advantage of cross-validationExercise 13: 10-fold cross-validationExercise 14: 5-fold cross-validationExercise 15: 5 x 5-fold cross-validationExercise 16: Making predictions on new dataWhat is DataCamp?
Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.