Group-membership Probabilities
In machine learning, a probabilistic classifier is a statistical classification, classifier that is able to predict, given an observation of an input, a probability distribution over a Set (mathematics), set of classes, rather than only outputting the most likely class that the observation should belong to. Probabilistic classifiers provide classification that can be useful in its own right or when combining classifiers into ensemble classifier, ensembles. Types of classification Formally, an "ordinary" classifier is some rule, or function (mathematics), function, that assigns to a sample a class label : :\hat = f(x) The samples come from some set (e.g., the set of all document classification, documents, or the set of all Computer vision#Bababoui, images), while the class labels form a finite set defined prior to training. Probabilistic classifiers generalize this notion of classifiers: instead of functions, they are conditional probability, conditional distributions \Pr(Y \v ...
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Machine Learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task (computing), tasks without explicit Machine code, instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed Neural network (machine learning), neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Statistics and mathematical optimisation (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysi ...
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