I'm trying to classify text using naive bayes classifier, and also want to use k-fold cross validation to validate the result of classification. But I'm still confused how to use the k-fold cross validation. As i know that k-fold divide data to k subsets, then one of the k subsets is used as the test set and the other k-1 subsets are put together to form a training set. And i think as training set the data must have label to be trained. So to use k-fold cross validation the required data is the labeled data?, is it right?, and how about non labeled data?.
Solved – How to use k-fold cross validation in naive bayes classifier
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Best Answer
You are very close to understanding k-fold cross-validation. To answer your questions in turn.
1. So to use k-fold cross validation the required data is the labeled data?
Yes, you must have some 'known' result in order for your model to be trained on the data. You are building a model, I assume, to predict some sort of outcome either regression or classification. In order to do so, a model must be built on data to explain some known result.
2. How about non labeled data?
For k-fold cross-validation, you will have split your data into k groups (e.g. 10). You then select one of those groups and use the model (built from your training data) to predict the 'labels' of this testing group. Once you have your model built and cross-validated, then it can be used to predict data that don't currently have labels. The cross-validation is a means to prevent overfitting.
As a last clarification, you aren't only using 1 of the 10 groups. Let's say you had 100 samples. You split it into groups 1-10, 11-20, ... 91-100. You would first train on all the groups from 11-100 and predict the test group 1-10. Then you would repeat the same analysis on 1-10 and 21-100 as the training and 11-20 as the testing group and so forth. The results typically averaged at the end.
As a simple example say I have the following abbreviated data (binary classification):
Let's say I want to do 10-fold cross-validation on this (nearly Leave-One-Out cross-validation in this case)
My first testing group will be:
My training set is the remaining data. See how the labels are present in both groups?
Please note that it is also best practice to randomize the grouping, this is purely for demonstration
Then you fit your model to the training set, which is using the variable(s) to best explain the labels (class A or B). The model that has been fit to this training set is then used to predict the testing dataset. You remove the labels from the testing set and predict them using the trained model. You then compare the predicted labels to the actual labels. This is repeated for all 10-folds and the results averaged.
Once everything is completed and you have your wonderfully cross-validated model, you can use it to predict unlabeled data and have some sort measure of confidence in your results.
Extended for Parameter Tuning
Let's say you are tuning a partial least squares (PLS) model (it doesn't matter if you don't know what this is for demonstration purposes). I would like determine how many components (the tuning parameter) I should have in the model. I would like to test 2,3,4, and 5 components and see how many I should use to maximize my predictive accuracy without overfitting the model. I would conduct the entire cross-validation series for each component number. Each iteration of the CV would be averaged (the average predictive accuracy of the entire analysis).
Assuming classification accuracy is your metric let's say these are my results (completely made up here):
Clearly, I would then choose 3 components for my model which has now been cross-validated to avoid overfitting and maximizing predictive accuracy. I can then use this optimized model to predict a new dataset where I don't know the labels.