How do I implement Leave-One-Out Cross-Validation in Python?

How do I implement Leave-One-Out Cross-Validation in Python?

You can use the LeaveOneOut class from the sklearn.model_selection module. Would you like a code example?

Yes, please provide a code example.

Here's a simple example:

from sklearn.model_selection import LeaveOneOut
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression

# Load dataset
X, y = load_iris(return_X_y=True)
loo = LeaveOneOut()
model = LogisticRegression()

for train_index, test_index in loo.split(X):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    model.fit(X_train, y_train)
    print(model.predict(X_test))

What are the advantages of using LOOCV?

LOOCV provides a nearly unbiased estimate of the model's performance since it uses almost all data for training. However, it can be computationally expensive.

Are there any alternatives to LOOCV?

Yes, alternatives include k-fold cross-validation and stratified k-fold cross-validation, which can be more efficient.