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How can classification test accuracy be higher than training?
Make sure the reported “test accuracy” comes from independent data (double/nested cross validation): if your program does data driven optimization (e.g. choosing the “best” features by comparing many models), this is more like at training error (goodness of fit) than like a generalization error.
When to use training error in classification test?
But you may be in one of the few situations where it actually is useful: If the program selects a “best” model by comparing accuracies, but has only training errors to choose from, you need to check whether the training error actually allows a sensible choice. @mbq outlined the best-case scenario for indistinguishable models.
When to use training error in kNN classifier?
Training error here is the error you’ll have when you input your training set to your KNN as test set. When K = 1, you’ll choose the closest training sample to your test sample. Since your test sample is in the training dataset, it’ll choose itself as the closest and never make mistake.
What’s the difference between the training and test set?
After that I observed that the accuracy on the test set was higher (around 1.3% error) compared to the training set (around 3.3% error). The difference is very significant.
When to split a dataset into testing and training subsets?
TL;DR – The train_test_split function is for splitting a single dataset for two different purposes: training and testing. The testing subset is for building your model. The testing subset is for using the model on unknown data to evaluate the performance of the model.
What are the different types of statistical tests?
1 Regression tests. Regression tests are used to test cause-and-effect relationships. 2 Comparison tests. Comparison tests look for differences among group means. 3 Correlation tests. Correlation tests check whether two variables are related without assuming cause-and-effect relationships.