Cross Validation
In Machine Learning splitting the dataset into training and testing might be troublesome sometimes. Cross Validation is a technique using which we select the batches of the different training sets and fit them into the model. This in return helps in generalizing the model and is less prone to overfitting. The commonly used Cross Validation methods are KFold, StratifiedKFold, RepeatedKFold, LeaveOneGroupOut, and GroupKFold.
We shall now implement the cross-validation technique to understand the ROC curve on different samples of the dataset.
Receiver Operating Characteristic (ROC) with Cross Validation in Scikit Learn
In this article, we will implement ROC with Cross-Validation in Scikit Learn. Before we jump into the code, let’s first understand why we need ROC curve and Cross-Validation in Machine Learning model predictions.