Random forest regression
Bagged trees for a numeric outcome, with holdout R² and RMSE.
When to use it
Use random forest regression when the outcome is numeric and you want a holdout R², RMSE, and feature importance without a linear model. The classification version is random forest classification. A linear fit of the same columns is linear regression.
Assumptions
Outcome and features are numeric. Holdout fraction 0.25, 100 trees, min_samples_leaf 5, no depth cap, random_state=0. HTTP and chat dispatch both honour test_fraction and min_samples_leaf.
Running it in Tensr
Options
Prop
Type
Reading the output
Exam score from hours and anxiety on that same 240-row sample. 100 trees, min_samples_leaf 5, no depth cap. Both slopes were built steep, so holdout R² should be positive.
| Variable | Importance |
|---|---|
| hours | 0.845 |
| anxiety | 0.155 |
Holdout R² = 0.721, RMSE = 4.733, n = 240, min_samples_leaf = 5. Hours and anxiety were built with steep slopes, so a positive holdout R² is the expected reading.
Reporting (APA 7)
Random forest regression of exam score on hours and anxiety, n = 240, holdout R² = 0.721, RMSE = 4.733.
Coming from SPSS
SPSS has no random-forest regression dialog. Tensr’s item is on the ML menu, Regression → Random Forest Regression.
Related
Linear regression is the parametric counterpart. Gradient boosting can be switched to regression mode.