supervised learning

Supervised learning, unsupervised learning, and reinforcement learning are three approaches to machine learning( ML). Supervised Learning is supervised learning where future developments can be inferred from the data.

Unsupervised learning is about drawing insights and inferences from the dataset, and reinforcement learning is where the system receives recognition when it has performed an appropriate action.

Supervised learning is about mapping the relationships between input and output variables. These are classified and learning algorithms are derived from them to support future decisions. Using training data and refined classification with group membership, the system improves the learning algorithm. During training, the system contains datasets with group membership from which the system can select the group features. The models built by Supervised Learning from existing data.

Applications of supervised learning include autonomous driving, chatbots, recognition systems, expert systems, and robots.