Avoiding class imbalances

Some data contains very imbalanced outcomes - like a rare disease dataset. When splitting randomly, you might end up with a very unfortunate split. Imagine all the rare observations are in the test and none in the training set. That would ruin your whole training process!

Fortunately, the initial_split() function provides a remedy. You are going to observe and solve these so-called class imbalances in this exercise.

There is already code provided to create a split object diabetes_split with a 75% training and 25% test split.

This exercise is part of the course

Machine Learning with Tree-Based Models in R

Hands-on interactive exercise

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

# Preparation
set.seed(9888)
diabetes_split <- initial_split(diabetes, prop = 0.75)

# Proportion of 'yes' outcomes in the training data
counts_train <- table(training(___)$outcome)
prop_yes_train <- counts_train["___"] / sum(counts_train)

# Proportion of 'yes' outcomes in the test data
counts_test <- table(___)
prop_yes_test <- ___ / sum(___)

paste("Proportion of positive outcomes in training set:", round(prop_yes_train, 2))
paste("Proportion of positive outcomes in test set:", round(prop_yes_test, 2))

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

Current Exercise

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