How do I implement cross-validation in Python?

How do I implement cross-validation in Python?

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

Yes, please provide a code example.

Sure! Here's a simple example:

from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier

# Load dataset
iris = load_iris()
X, y = iris.data, iris.target

# Create a model
model = RandomForestClassifier()

# Perform cross-validation
scores = cross_val_score(model, X, y, cv=5)
print('Cross-validation scores:', scores)

This will give you the accuracy scores for each fold.

What does the 'cv' parameter do in the cross_val_score function?

The 'cv' parameter specifies the number of folds in the cross-validation. For example, cv=5 means the data will be split into 5 parts, and the model will be trained and tested 5 times, each time using a different part as the test set.