What Does Training Data Mean?

A machine learning model is taught using training data, which is a very big dataset. Prediction models that employ machine learning algorithms are taught how to extract attributes related to certain business objectives using training data. The training data is labelled for supervised ML models. Unsupervised machine learning models are trained with unlabeled data.

The concept of using training data in machine learning systems is a simple one, yet it is critical to how these technologies function. The training data is a set of data used to teach software how to learn and deliver advanced outcomes using technologies like neural networks. It could be supplemented with other data sets known as validation and testing sets.

The training set is the material through which the computer learns how to process information. Machine learning uses algorithms – it mimics the abilities of the human brain to take in diverse inputs and weigh them, in order to produce activations in the brain, in the individual neurons. Artificial neurons replicate a lot of this process with software – machine learning and neural network programs that provide highly detailed models of how our human thought processes work.

With that in mind, training data can be structured in different ways. For sequential decision trees and those types of algorithms, it would be a set of raw text or alphanumerical data that gets classified or otherwise manipulated. On the other hand, for convolutional neural networks that have to do with image processing and computer vision, the training set is often composed of large numbers of images. The idea is that because the machine learning program is so complex and so sophisticated, it uses iterative training on each of those images to eventually be able to recognize features, shapes and even subjects such as people or animals. The training data is absolutely essential to the process – it can be thought of as the “food” the system uses to operate.