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Provides many tasks from classification to QA, and various languages from English, Portuguese to Arabic. Appen : Off The Shelf and Open Source Datasets hosted and maintained by the company. These biological, image, physical, question answering, signal, sound, text, and video resources number over 250 and can be applied to over 25 different use ...
Since no single form of classification is appropriate for all data sets, a large toolkit of classification algorithms has been developed. The most commonly used include: [ 9 ] Artificial neural networks – Computational model used in machine learning, based on connected, hierarchical functions Pages displaying short descriptions of redirect ...
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The template for any binary confusion matrix uses the four kinds of results discussed above (true positives, false negatives, false positives, and true negatives) along with the positive and negative classifications.
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning.In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations.
Binary probabilistic classifiers are also called binary regression models in statistics. In econometrics, probabilistic classification in general is called discrete choice. Some classification models, such as naive Bayes, logistic regression and multilayer perceptrons (when trained under an appropriate loss function) are
Binary classification is the task of classifying the elements of a set into one of two groups (each called class). Typical binary classification problems include: Medical testing to determine if a patient has a certain disease or not; Quality control in industry, deciding whether a specification has been met;
Data classification can be viewed as a multitude of labels that are used to define the type of data, especially on confidentiality and integrity issues. [1] Data classification is typically a manual process; however, there are tools that can help gather information about the data. [2] Data sensitivity levels are often proposed to be considered. [2]