Compare the fit and misclassification rates (including the churner misclassification rates) when using a minimum split size of 5 and 20.
To compare the fit and misclassification rates when using a minimum split size of 5 and 20 in a decision tree model, we need to train two separate models with these different parameters and evaluate their performance.
Let's assume we are using a dataset to predict customer churn, where the target variable is whether a customer churned or not. We split the data into training and testing sets and train two decision tree models with minimum split sizes of 5 and 20.
After training the models, we can evaluate their fit and misclassification rates on the testing set. The fit rate can be measured by the overall accuracy of the model, while the misclassification rate can be calculated as (1 - accuracy).
Additionally, we can specifically look at the misclassification rates for predicting churners, which is the percentage of churners that are incorrectly classified by the model.
By comparing the fit and misclassification rates of the two models, we can determine which minimum split size results in a better-performing model. A smaller minimum split size may lead to a more complex decision tree that is prone to overfitting, while a larger minimum split size may result in a simpler tree that can generalize better to new data.