What is cross-validation in scikit-learn?

What is cross-validation in scikit-learn?

Cross-validation is a technique used to assess the performance of a model by dividing the dataset into subsets, training the model on some subsets and validating it on others.

Why is cross-validation important?

It's important because it helps to ensure that the model generalizes well to unseen data, reducing the risk of overfitting.

How do I implement cross-validation in scikit-learn?

You can use the cross_val_score function from scikit-learn, which allows you to specify the model, dataset, and number of folds for validation.

Can you give me an example of using cross-validation?

Sure! Here's a simple example:

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

model = RandomForestClassifier()
score = cross_val_score(model, X, y, cv=5)
print(score)