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This article introduces how k-fold cross-validation in R language is. The content is very detailed. Interested friends can refer to it for reference. I hope it can help you.

"Machine learning needs to divide the data into training set and test set, so how to divide the training set and test set becomes an important factor affecting the model effect. This paper introduces a common method for dividing optimal training set and test set--k fold cross validation. "

K-fold cross-validation

K-fold cross-validation first divides all data into K sub-samples, selects one of them as test set without repetition, and uses the other K-1 samples for training. Repeat K times, average K times, or use other metrics to get a single estimate.

The advantage of this approach is that it ensures that every subsample is trained and tested, reducing generalization errors. Among them, 10 fold cross validation is the most commonly used.

example code

In Linear Classifier and Performance Evaluation (R Language), we randomly select 70% of the dataset as the training set and the remaining 30% as the test set. We make predictions by linear regression and evaluate the model effect by ROC and AUC. Now, we use k-fold cross-validation to select the optimal training and test sets, build linear classifiers and evaluate the model performance.

1. Data import and grouping. Import the data and use the createFolds() function in the caret package to divide the data into 10 parts based on the label column.

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