Make predictions

Making predictions with data is one of the fundamental goals of machine learning. Now that you know how to split the data and fit a model, it's time to make predictions about unseen samples with your models.

You are going to make predictions about your test set using a model obtained by fitting the training data to a tree specification.

Available in your workspace are the datasets that you generated previously (diabetes_train and diabetes_test) and a decision tree specification tree_spec, which was generated using the following code:

tree_spec <- decision_tree() %>%
  set_engine("rpart") %>%
  set_mode("classification")

This exercise is part of the course

Machine Learning with Tree-Based Models in R

Exercise instructions

Fit your specification to the training data using outcome as the target variable and all predictors to create model.
Use your model to predict the outcome of diabetes for every observation in the test set and assign the result to predictions.
Add the true test set outcome to predictions as a column named true_class and save the result as predictions_combined.
Use the head() function to print the first rows of the result.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Train your model
model <- tree_spec %>% 
  ___

# Generate predictions
predictions <- ___(model,
                   ___)

# Add the true outcomes
predictions_combined <- predictions %>% 
  ___(true_class = ___)

# Print the first lines of the result
___

Edit and Run Code

This exercise is part of the course

Machine Learning with Tree-Based Models in R

IntermediateSkill Level

4.9+

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Ready to build a real machine learning pipeline? Complete step-by-step exercises to learn how to create decision trees, split your data, and predict which patients are most likely to suffer from diabetes. Last but not least, you’ll build performance measures to assess your models and judge your predictions.

Exercise 1: Welcome to the course!Exercise 2: Why tree-based methods?Exercise 3: Specify that tree Exercise 4: Train that model Exercise 5: How to grow your tree Exercise 6: Train/test split Exercise 7: Avoiding class imbalances Exercise 8: From zero to hero Exercise 9: Predict and evaluate Exercise 10: Make predictions

Current Exercise

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