Data and Artificial intelligence often go hand in hand, as it serves as a crucial component in the way machines learn. As the golden saying goes “Garbage in, garbage out”. In training data sense, “Garbage training data in, garbage model out”.
Data forms the basis for Machine Learning (ML) algorithms and Artificial Intelligence (AI). Most Machine Learning models learn from the data they consume to produce accurate results. In turn, the data you use will determine the results of your models. Because a machine learning model learns to find patterns in the input that is fed to it and that input is known as training data.
What is Training Data?
Training data or also called training dataset is the foundation of how ML models work, it is defined as the first set of data used to help ML models make predictions. Imagine training data as the manual guide that helps teach AI to do its assigned task in a repetitive process to fine-tune its predictions.
The training of machines happens when we feed it with accurately labelled data. This then helps the algorithms to connect the patterns in the data to identify the right answer.
Why quality training data matters?
For these systems to learn and formulate the right responses to build an accurate ML model, it’s essential to build a comprehensive, high-quality training dataset. The two main factors that come into play for the success of your ML models lies in the quality and quantity of your training data.
Especially in supervised machine learning algorithms, training data needs to be labelled with the right information. As machine learning models need data to understand the world around them, labelled training data provides information and context that may not be known to the algorithms.
For example, if you want to teach the algorithm to differentiate between an empty retail box and a non-empty retail box, you could give it thousands, if not millions of examples of each instance, respectively. The algorithm will then pick up the patterns or characteristics of the different boxes over time and be able to distinguish between the two. Thus, the quality of your training data is crucial as it will reflect and affect the accuracy of your models.
It’s undeniable that the success and accuracy of a machine learning algorithm is heavily dependent on what it learns from — training data.
Understanding the importance and prioritizing the quality of your training data will help you a great deal in achieving accuracy in your models. The first step in gaining good quality training data begins with finding the right processes and tools to label your training data.
Find out how Supahands can help you in achieving accuracy in your models with high-quality training data.