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A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Professional societies such as the American Thoracic Society and the European Respiratory Society have published guidelines regarding the conduct and interpretation of pulmonary function testing to ensure standardization and uniformity in performance of tests. The interpretation of tests depends on comparing the patients values to published ...
Xi designed ATS “in an attempt to combine specification and implementation into a single programming language.” [4] ATS is derived mostly from the languages ML and OCaml. An earlier language, Dependent ML, by the same author has been incorporated into ATS. The first implementation, ATS/Proto (ATS0), was written in OCaml and was released in ...
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. [1] AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment.
Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. [1] In the context of machine learning and more generally statistical analysis, this may be the selection of a statistical model from a set of candidate models, given data.
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Decision trees are a popular method for various machine learning tasks. Tree learning is almost "an off-the-shelf procedure for data mining", say Hastie et al. , "because it is invariant under scaling and various other transformations of feature values, is robust to inclusion of irrelevant features, and produces inspectable models.