Contents
Can we use same data for training and testing?
The only danger of reusing the same test data is that you might change the model (e.g., adding another layer, and/or adding more units to an existing layer) because it gives you a better result on your test data. When you alter your model in response to observations of the test error, you risk overfitting to your data.
Why do you need to separate training and test datasets?
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.
What are the training and testing stages of data?
Training and Test Sets: Splitting Data
- Figure 1. Slicing a single data set into a training set and test set.
- Figure 2. Validating the trained model against test data.
- Never train on test data.
Why is it wrong to train and test a model?
It can happen that the model you train learns “too much” or memorizes the training data, and then it performs poorly on unseen data. This is called “overfitting”. 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.
When to use a test or train dataset?
Test Dataset. Test Dataset: The sample of data used to provide an unbiased evaluation of a final model fit on the training dataset. The Test dataset provides the gold standard used to evaluate the model. It is only used once a model is completely trained (using the train and validation sets).
Can a model perform well on test data?
If that were not the case, then one could perfectly get a model that performs well in training data but does not on test data. And it would not be because of overfitting of the training data.
Why are predictive models chosen on training data?
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely have lower accuracy on an unseen test dataset.