Why do we use training and test set?

Training data is the set of the data on which the actual training takes place. Validation split helps to improve the model performance by fine-tuning the model after each epoch. The test set informs us about the final accuracy of the model after completing the training phase.

What is a training data set used for?

Training data (or a training dataset) is the initial data used to train machine learning models. Training datasets are fed to machine learning algorithms to teach them how to make predictions or perform a desired task.

What is the purpose of a test dataset?

Test Dataset: The sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.

What is test and train?

What is Train/Test. Train/Test is a method to measure the accuracy of your model. It is called Train/Test because you split the the data set into two sets: a training set and a testing set. 80\% for training, and 20\% for testing. You train the model using the training set.

What is test and train data?

What is training and testing accuracy?

Training accuracy means that identical images are used both for training and testing, while test accuracy represents that the trained model identifies independent images that were not used in training.

Would you expect the training or the test accuracy to be better why?

Test accuracy should not be higher than train since the model is optimized for the latter. Ways in which this behavior might happen: you did not use the same source dataset for test. You should do a proper train/test split in which both of them have the same underlying distribution.

Why is my test accuracy higher than training?

How to interpret a test accuracy higher than training set accuracy. Most likely culprit is your train/test split percentage. Imagine if you’re using 99\% of the data to train, and 1\% for test, then obviously testing set accuracy will be better than the testing set, 99 times out of 100.