How to check model’s AUC score using Cross Validation in Python
When building a machine learning model, it’s important to evaluate its performance using various metrics. One of them is the AUC (Area Under the Curve) score, which measures the model’s ability to predict binary outcomes.
Cross-validation is a method that allows to test the model’s AUC score by dividing the data into several parts, training the model on some of the parts and evaluating it on the others.
In Python, the library scikit-learn provides an easy way to perform cross-validation using the cross_val_score function and specifying ‘roc_auc’ as a scoring metric.
The first step is to import the library and load the dataset into a pandas dataframe. Then, create an instance of the model you want to evaluate.
After that, you can use the cross_val_score function to evaluate the model’s AUC score. The function takes the model, the dataset, and the number of folds (parts) you want to divide the data into. The function returns an array of scores, where each score represents the AUC score of the model on one fold.
It’s also worth mentioning that, you can use ‘cv’ parameter that takes the number of splits you would like to make, or an iterable that you can use to define the splits.
Finally, you can use the mean() method to calculate the average AUC score of the model, this gives an overall measure of the model’s performance.
In summary, cross-validation and AUC score are powerful tools for evaluating the performance of a machine learning model. By using the cross_val_score function in scikit-learn and specifying ‘roc_auc’ as a scoring metric, it’s easy to perform cross-validation and check the AUC score of the model in Python, making it a valuable tool for data scientists and machine learning practitioners.
In this Learn through Codes example, you will learn: How to check model’s precision score using Cross Validation in Python.
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