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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]
P300 Interface Dataset Data from nine subjects collected using P300-based brain-computer interface for disabled subjects. Split into four sessions for each subject. MATLAB code given. 1,224 Text Classification 2008 [264] [265] U. Hoffman et al. Heart Disease Data Set Attributed of patients with and without heart disease.
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
If a web server responds with Cache-Control: no-cache then a web browser or other caching system (intermediate proxies) must not use the response to satisfy subsequent requests without first checking with the originating server (this process is called validation). This header field is part of HTTP version 1.1, and is ignored by some caches and ...
Data type validation is customarily carried out on one or more simple data fields. The simplest kind of data type validation verifies that the individual characters provided through user input are consistent with the expected characters of one or more known primitive data types as defined in a programming language or data storage and retrieval ...
Verification is intended to check that a product, service, or system meets a set of design specifications. [6] [7] In the development phase, verification procedures involve performing special tests to model or simulate a portion, or the entirety, of a product, service, or system, then performing a review or analysis of the modeling results.
OpenML may refer to: . OpenML (Open Machine Learning), an open science online platform for machine learning, which holds open data, open algorithms and tasksOpenML (Open Media Library), a free, cross-platform programming environment designed by the Khronos Group for capturing, transporting, processing, displaying, and synchronizing digital media (2D and 3D graphics, audio and video processing ...
Cross-validation, [2] [3] [4] sometimes called rotation estimation [5] [6] [7] or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set. Cross-validation includes resampling and sample splitting methods that use different ...