In scikit-learn’s GridSearchCV (Grid Search Cross Validation), the parameter cv stands for “cross-validation.” It determines the cross-validation splitting strategy to be used when evaluating the performance of a machine learning model.
When cv is set to an integer (e.g., cv=5), it represents the number of folds in a (Stratified) K-Fold cross-validation. For example, cv=5 means that the dataset will be divided into 5 equal-sized folds, and the model training and evaluation will be performed 5 times. Each time, one of the folds will be used as the test set, and the remaining folds will be used as the training set.
Here’s an example of using GridSearchCV with a decision tree classifier and 5-fold cross-validation:
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris
# Load the iris dataset
iris = load_iris()
# Create a decision tree classifier
clf = DecisionTreeClassifier()
# Define the parameter grid to search
param_grid = {'max_depth': [2, 3, 4, 5]}
# Create a GridSearchCV object with 5-fold cross-validation
grid_search = GridSearchCV(clf, param_grid, cv=5)
# Fit the model with the cross-validated grid search
grid_search.fit(iris.data, iris.target)
# Print the best parameters found during the grid search
print("Best Parameters:", grid_search.best_params_)
In this example, the model will be trained and evaluated 5 times (5-fold cross-validation) for each combination of hyperparameters specified in the param_grid. The GridSearchCV will then identify the best combination of hyperparameters based on the average performance across all folds.
Adjusting the value of cv can impact the robustness of the model evaluation. A higher number of folds can provide a more stable estimate of the model’s performance but may also require more computational resources. Conversely, a lower number of folds may be faster but could be more sensitive to the specific split of the data. It’s a trade-off between computational cost and the reliability of the evaluation.
