Supervised learning
Supervised learning is one of the most common methods for machine learning, in which an algorithm is trained to learn patterns and relationships between input data and the corresponding output labels. In this process, the algorithm is guided by pre-labeled examples that serve as the basis for learning the model.
How does supervised learning work?
Supervised learning works by using labeled data to train the algorithm. The process begins by collecting a dataset of input data and its corresponding output labels. The algorithm will then analyze this data and create a model that understands the patterns between the input data and the output labels. The goal of the model is to make accurate predictions about new, unseen data. The algorithm adjusts the parameters of the model by passing through the input data and comparing the corresponding output labels with the model's predictions. The algorithm then adjusts the parameters to improve the accuracy of the predictions.