About this document
K-Fold Cross Validation Explained by VamshikrishnaReddy is a document available to read on EtoBox.
k-fold cross validation is a technique to estimate the performance of a classifier by dividing the training data into k folds, using k-1 folds to train the classifier and the remaining fold to test it, repeating this for each fold and averaging the results. The estimate for the algorithm performance is the average error across folds with the standard deviation capturing variability in the estimates from running k-fold cross validation multiple times with different random arrangements of data.
- Author
- VamshikrishnaReddy
- Language
- EN