What does cross-validation do in Weka?
According to “Data Mining with Weka” at The University of Waikato: Cross-validation is a way of improving upon repeated holdout. Cross-validation is a systematic way of doing repeated holdout that actually improves upon it by reducing the variance of the estimate.
What is use training set in Weka?
Training data refers to the data used to “build the model”. For example, it you are using the algorithm J48 (a tree classifier) to classify instances, the training data will be used to generate the tree that will represent the “learned concept” that should be a generalization of the concept.
Does cross-validation reduce error?
Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers by choosing classifier parameter values that minimize the CV error estimate.
What is training error and validation error?
Training error is averaged over whole epoch, rather all at once at the end of the epoch, but validation error is only at end of epoch. As we sample our training data to compute gradients, we might as well compute the loss over them as well.
What is the purpose of performing cross-validation?
The goal of cross-validation is to test the model’s ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias and to give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem).
What does 10 fold cross-validation mean in Weka?
With 10-fold cross-validation, Weka invokes the learning algorithm 11 times, once for each fold of the cross-validation and then a final time on the entire dataset. A practical rule of thumb is that if you’ve got lots of data you can use a percentage split, and evaluate it just once.
Why is cross validation better than simple train test split?
Cross-validation is usually the preferred method because it gives your model the opportunity to train on multiple train-test splits. This gives you a better indication of how well your model will perform on unseen data. That makes the hold-out method score dependent on how the data is split into train and test sets.
How do you make a Weka training set?
In the Explorer just do the following: training set: Load the full dataset….test set:
- Load the full dataset (or just use undo to revert the changes to the dataset)
- select the RemovePercentage filter if not yet selected.
- set the invertSelection property to true.
- apply the filter.
- save the generated data as new file.
Why is cross validation unbiased?
The cross-validation estimator F* is very nearly unbiased for EF. The reason that it is slightly biased is that the training set in cross-validation is slightly smaller than the actual data set (e.g. for LOOCV the training set size is n − 1 when there are n observed cases).
What is the difference between training set validation set and test set?
The “training” data set is the general term for the samples used to create the model, while the “test” or “validation” data set is used to qualify performance. Perhaps traditionally the dataset used to evaluate the final model performance is called the “test set”.
What is cross validation error?
Cross-Validation is a technique used in model selection to better estimate the test error of a predictive model. The idea behind cross-validation is to create a number of partitions of sample observations, known as the validation sets, from the training data set.