What is cross_val_score?

cross_val_score is a function in Scikit Learn that will help you to perform cross-validation scoring of an estimator. Generally, cross-validation helps us to understand how well a model has generalized to an independent dataset.

You need to provide the following parameters as an input:

• estimator
• input features
• target values
• other optional parameters

An estimator is a machine learning model on which you train your dataset. Input features are the independent variables and target value is a dependent variable that we have to determine. There are other optional parameters like cv, scoring, n_jobs which you can check in scikit learn documentation.

When we pass all these parameters to the function, it will perform k-fold cross-validation. Here, your dataset is split into k subsets (folds), and the model is trained and evaluated k times. Each time a different fold is chosen as test set and remaining are chosen as train set.

As a result, you get an array of k values, where each value determines how the model performed on that fold based on the scoring metric.

In scikit learn, we can demonstrate multi-metric evaluation with the help of two functions cross_val_score and GridSearchCV. They help you check the performance of the model based on multiple metrics with a single click rather than writing repetitive code. In this article, we will first discuss the implementation of cross_val_score and then GridSearchCV. Then finally, we will see how they can work together.