Also known as: sl Β· Supervised Machine Learning
Supervised learning is a machine learning approach in which a model learns from examples that pair inputs with known target outputs or labels. The model uses those labeled examples to learn mappings that can support prediction, classification, or regression on new data.
The machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from '' consisting of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal''). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instance
A way of training machine learning systems for a specific application. In a training phase, an AI system is fed labelled data. The system trains from the input data, and the resulting model is then tested to see if it can correctly apply labels to new unlabelled data (such as if it can correctly label unlabelled pictures of cats and dogs accordingly). This type of learning is useful when it is clear what is being searched for, such as identifying spam mail. See also semi-supervised learning, unsupervised learning, reinforcement learning and training datasets.
is a type of AI, or machine learning, that operates on data. With supervised learning, we have a specific question in mind, like 'Does this photo have a cat in it?' To answer this question with a computer, we collect lots of photos, many with cats and many without. This is our data, which we can use to train our algorithm. The most important part of supervised learning is that we understand the task fully, and can thus label each image ourselves, 'Has cat' or 'No cat.' We can then feed this labeled training data through a supervised learning algorithm, which learns to apply this same label to new images.
A side-by-side comparison of Supervised Learning and Unsupervised Learning. Understand how learning from labeled examples differs from finding patterns in data without pre-existing labels.
A side-by-side comparison of Supervised Learning and Semi-Supervised Learning. Understand how learning from labeled examples differs from combining labeled and unlabeled data.
A side-by-side comparison of Reinforcement Learning and Supervised Learning. It explains how learning from rewards in an environment differs from learning mappings from labeled examples.
A side-by-side comparison of Active Learning and Supervised Learning. It explains how selectively requesting the most useful labels differs from training on an existing labeled dataset.