Contents
Should validation set be balanced?
Since balancing the test dataset is not allowed, the validation dataset can not be validated too. Additionally, I should mention that when you balance the test dataset, you will get a better performance in comparison to using an unbalanced dataset for testing.
Why is it important to keep testing and training set separate?
Separating data into training and testing sets is an important part of evaluating data mining models. Because the data in the testing set already contains known values for the attribute that you want to predict, it is easy to determine whether the model’s guesses are correct.
How large should my validation set be?
for very large datasets, 80/20% to 90/10% should be fine; however, for small dimensional datasets, you might want to use something like 60/40% to 70/30%.
What to do when training and testing data come from different?
An alternative is to make the dev/test sets come from the target distribution dataset, and the training set from the web dataset. Say you’re still using 96:2:2% split for the train/dev/test sets as before.
When to balance classes in a training data set?
You need to concentrate more metrics like Precision (PPR), Recall (sensitivity). This two metrics should be balanced when compare. Also you should have to check F1-Score which is harmonic mean of Precision and recall.
What are the metrics for working with imbalanced classes?
There are metrics that have been designed to tell you a more truthful story when working with imbalanced classes. Precision: A measure of a classifiers exactness. Recall: A measure of a classifiers completeness F1 Score (or F-score): A weighted average of precision and recall.
What to do about imbalanced classification dataset?
Again, if you are using scikit-learn and logistic regression, there’s a parameter called class-weight. Set this to balanced. Selection of evaluation metric also plays a very important role in model selection. Accuracy never helps in imbalanced dataset.