class rebalancedcv.RebalancedLeaveOneOutRegression()
Rebalanced Leave-One-Out cross-validator for regression.
Provides train/test indices to split data in train/test sets. Each sample is used once as a test set (singleton) while the remaining samples are used to form the training set, with subsampling to ensure similar label balances for all training sets across all splits.
This class is designed to have the same functionality and
implementation structure as scikit-learn’s
LeaveOneOut()
At least three observations are needed for
RebalancedLeaveOneOutRegression.
### Observing the indices on a small example dataset
import numpy as np
np.random.seed(1)
from rebalancedcv import RebalancedLeaveOneOutRegression
X = np.array([[1, 2, 1, 2], [3, 4, 3, 4]]).T
y = np.array([1.9, 2.2, 2.4, 2.5])
rloo = RebalancedLeaveOneOutRegression()
for i, (train_index, test_index) in enumerate(rloo.split(X, y)):
print(f"Fold {i}:")
print(f" Train: index={train_index}")
print(f" Test: index={test_index}")Fold 0:
Train: index=[1 3]
Test: index=[0]
Fold 1:
Train: index=[2 3]
Test: index=[1]
Fold 2:
Train: index=[1 3]
Test: index=[2]
Fold 3:
Train: index=[0 2]
Test: index=[3]The methods of the RebalancedLeaveOneOutRegression class
are designed to enable identical funcitonality to scikit-learn’s LeaveOneOut.
n_samples is the number of samples
and n_features is the number of features.n_samples is the number of samples
and n_features is the number of features.