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Why would it be a problem if your training set and test set are the same?
The problem of training and testing on the same dataset is that you won’t realize that your model is overfitting, because the performance of your model on the test set is good. The purpose of testing on data that has not been seen during training is to allow you to properly evaluate whether overfitting is happening.
Why is it important to keep test and training sets 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.
Can training and test set be same?
A test data set is a data set that is independent of the training data set, but that follows the same probability distribution as the training data set. A test set is therefore a set of examples used only to assess the performance (i.e. generalization) of a fully specified classifier.
What should be the split between train and test?
This will be our test group. And if we obtain a much worse score on our test set, than in our train group, then probably we’re overfitting our training data. Usually a 80–20 or 70–30% train-test split is considered reasonable.
What happens if you set true to false in train test?
The default value for this parameter is ‘True’, but if by mistake or ignorance we set it to ‘False’ and we split our data 80–20, we’ll end up training our model with a dataset with 500 males and 300 females, and testing it with a dataset only containing 200 females within it.
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.
Is there a recommendation system for train test?
We saw a gentle intr o duction to the topic and also an introduction to the most important similarity measures around it (remember that the whole repository about recommendation system and other projects are always available on my GitHub profile ). And yes, I know, there’s sooo much else around the topic, so we will come back to it in brief.