Fits a 100-tree Random Forest on training data and reports a
classification report (precision / recall / F1 / support per class)
on the held-out test set. Mirrors morie.ml.eval_robustness.
Usage
morie_ml_eval_robustness(
X,
y,
test_X,
test_y,
n_estimators = 100L,
random_state = 42L
)
Arguments
- X
Training features (data.frame or matrix).
- y
Training labels (factor or coercible to factor).
- test_X
Test features.
- test_y
Test labels.
- n_estimators
Number of trees. Default 100.
- random_state
Integer seed. Default 42.
Value
Named list keyed by class label and accuracy with
precision / recall / f1-score / support per class, mirroring
sklearn's classification_report(output_dict=True).