group-wise cross-validation

Group-wise cross-validation is a type of cross-validation that is used when the data has a group structure. It is a more appropriate approach when the samples are collected from different subjects, experiments, or measurement devices.

In group-wise cross-validation, the data is divided into groups, and the validation process is performed on each group separately. This ensures that the model is evaluated on data from different groups, which helps to assess its generalization performance in real-world scenarios.

Here is an example of how group-wise cross-validation can be implemented using the K-fold cross-validation technique:

from sklearn.model_selection import GroupKFold
from sklearn.linear_model import LogisticRegression

# Assuming we have features X, labels y, and groups g
X = ...
y = ...
groups = ...

# Create a group-wise cross-validation iterator
gkf = GroupKFold(n_splits=5)

# Initialize a model
model = LogisticRegression()

# Perform group-wise cross-validation
for train_index, test_index in gkf.split(X, y, groups):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    
    # Fit the model on the training data
    model.fit(X_train, y_train)
    
    # Evaluate the model on the test data
    score = model.score(X_test, y_test)
    
    # Print the evaluation score
    print("Validation score: ", score)

In this example, the data is divided into 5 groups using the GroupKFold function. The model is then trained and evaluated on each group separately. The evaluation score for each fold is printed to assess the model's performance.