There are various ways by which a model can be evaluated:
- Split test: In a split test, the dataset is divided into two parts, one is the training set and the other is test dataset. Once data is split the algorithm will use the training set and a model is created. The accuracy of a model is tested using the test dataset. The ratio of dividing the dataset in training and test can be decided on basis of the size of the dataset. It is fast and great when the dataset is of large size or the dataset is expensive. It can produce different result on how the dataset is divided into the training and test dataset. If the date set is divided in 80% as a training set and 20% as a test set, 60% as a training set and ...