Decision tree
Classify a categorical outcome with a simple tree and a train/test split.
When to use it
Use a decision tree when the outcome is a category and you want a readable split of numeric predictors, with holdout accuracy. This page uses the weak exam-cohort signal on purpose so the unpruned tree overfits. An ensemble of a stronger version of the same problem is random forest classification.
Assumptions
The outcome is categorical. Predictors are numeric. Classes with fewer than two rows are dropped. The holdout fraction is 0.25 on the HTTP body and in chat. The service clamps it to 0.10–0.50 and adds a note when it does. Maximum depth defaults to 5. Clear the field (HTTP null) for an unrestricted tree. Minimum leaf size defaults to 5. There is no pruning control. This page sets max_depth to None and min_samples_leaf to 1 so the tree can overfit.
Running it in Tensr
Options
Prop
Type
Reading the output
The 96-student exam cohort, pass or fail from hours and anxiety, 25% holdout. max_depth is set to None and min_samples_leaf to 1 so the tree is unrestricted; the dialog defaults are 5 and 5. Hours only shifts the pass rate a little, so this page is the overfitting example: train accuracy near 1 and test accuracy near chance.
| Variable | Importance |
|---|---|
| hours | 0.431 |
| anxiety | 0.569 |
Train accuracy = 1, test accuracy = 0.417, n = 96. This page is the overfitting example on purpose. max_depth is set to None and min_samples_leaf to 1 (the dialog defaults are 5 and 5), hours only shifts the pass rate a little, and 72 training rows are enough to memorise the sample. A large train–test gap, with test accuracy near 0.5, is the reading.
Reporting (APA 7)
Holdout classification of passed from hours and anxiety, n = 96. Test accuracy = 0.417, train accuracy = 1. With max_depth set to None and min_samples_leaf to 1, the unpruned tree overfits this weak exam-cohort signal.
Coming from SPSS
SPSS is Analyze → Classify → Tree. Tensr’s Analyze menu is Classification → Decision Tree Classification. The path string stored for this item matches that SPSS path. There is no CHAID or CRT method switch.
Related
Random forest classification averages many trees. Logistic regression is the parametric binary model.