What is a dataset?
A dataset is a collection of data. In other words, a dataset corresponds to the contents of a single database table or a single statistical data matrix, where every column of the table represents a particular variable, and each row corresponds to a given member of the data set in question. The dataset is used to train the model for performing various actions in machine learning.
What are the different types of datasets in machine learning?
A dataset can be split into many parts in a machine learning model. Generally, the dataset is divided into three parts, which is as follows:
- Training dataset:The sample of data used to train an algorithm to understand how to apply concepts such as neural networks to learn and produce results. It includes both input data and the expected output. It is the sample of data used to fit the model.
- Validation dataset:The sample of data used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters. The evaluation becomes more biased as a skill on the validation dataset is incorporated into the model configuration.
- Test dataset:The sample of data used to evaluate how well the algorithm was trained with the training dataset. In AI projects, we can’t use the training dataset in the testing stage because the algorithm will already know in advance the expected output, which is not our goal.