Before digging deeper into this subject and comparison, let’s first make understandable main terms in it.
- Training data- This type of data is being used as one to feed the Machine Learning algorithm and processing in order for data outcome to be a purpose for a desirable result. This data is labeled and evaluated before incorporating it into the ML process of learning.
- Validation data- Data that is being incorporated for the first time and hasn’t been evaluated before. Incorporating this type of data into Machine Learning model training, data scientists are measuring the prediction capabilities of the model, based on a new dataset. It is a common but not obligatory part of a Machine Learning training process.
- Test data- Data used for final testing of model performances and capabilities. If validation and training data are being labeled, this one shouldn’t be. Positive feedback and getting desirable, valid data outcome is confirmation that the model has been trained effectively.
All of them have their own specific duties in Machine Learning model training but it’s easy to notice occasional overlaps between them.
Effective ML training requires high-quality data, algorithm hygiene, and a data scientist(s) with a proven record of previous similar projects successfully finished. Algorithm hygiene is the process of research, gathering the data, and unbiaising it before incorporating it into Machine Learning model training. And in the end but not less important, a lot of quality training. Different datasets will serve different purposes and, once the Machine understands the desirable directions, it will improve its prediction possibilities, and regarding them, the data outcomes will be more precise, highly evaluated, and trustful for further activities.
Knowing all mentioned above answer to your question is simple: The difference between training data and test data is that the first one trains the model, and the other serves as a confirmation of its corr