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Which is better ten fold or two fold cross validation?
The measures we obtain using ten-fold cross-validation are more likely to be truly representative of the classifiers performance compared with twofold, or three-fold cross-validation. This is so, because each time we train the classifier we are using 90% of our data compared with using only 50% for two-fold cross-validation.
Can a test set reflect the accuracy of a classifier?
In either case, the accuracy based on a single test set may not reflect the true accuracy when our classifier is used with new data. A solution to this problem might be to repeat the process a number of times and average the results. For example, we might divide the data in half.
How does the compare ROCS operator work in Excel?
This operator generates ROC charts for the models created by the learners in its subprocess and plots all the charts in the same plotter for comparison. The Compare ROCs operator is a nested operator i.e. it has a subprocess. The operators in the subprocess must produce a model. This operator calculates ROC curves for all these models.
What’s the difference between validation and test sets?
The difference between validation and test sets (and their corresponding accuracies) is that validation set is used to build/select a better model, meaning it affects the final model.
When to divide data into training and validation?
If the test set is locked away, but you still want to measure performance on unseen data as a way of selecting a good hypothesis, then divide the available data (without the test set) into a training set and a validation set.
What’s the difference between validation and test accuracy?
No. It is a [estimate of] test accuracy. The difference between validation and test sets (and their corresponding accuracies) is that validation set is used to build/select a better model, meaning it affects the final model.