Training data is the initial data on which machine learning algorithms are developed. Breadth and quality of data directly impacts the performance quality of any machine learning model. Machine learning models create and refine rules around the training data they have used. There are three approaches to using training data in machine learning:
Supervised learning – humans choose the data features and enrich or annotate the data to help the machine recognize outcomes
Unsupervised learning – the data is unlabeled, with the machine czech republic mobile database identifying patterns in it
Hybrid learning – the machine learns from both labeled and unlabeled data
What affects the quality of training data?
Training a machine learning model with training data is similar to teaching a student with textbooks. The textbooks (training data) are filled with knowledge (patterns and relationships) that the student (machine learning model) needs to internalize. If the textbooks used are accurate, comprehensive, and diverse, a student will be better prepared to answer questions on a test. Similarly, if the training data is high quality and robust, the machine learning model will be better at making accurate predictions.