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  2. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    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]

  3. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    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 ...

  4. Verification and validation - Wikipedia

    en.wikipedia.org/wiki/Verification_and_validation

    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.

  5. Verification and validation of computer simulation models

    en.wikipedia.org/wiki/Verification_and...

    A requirement is that both the system data and model data be approximately Normally Independent and Identically Distributed (NIID). The t-test statistic is used in this technique. If the mean of the model is μ m and the mean of system is μ s then the difference between the model and the system is D = μ m - μ s. The hypothesis to be tested ...

  6. File:Overfitting on Training Set Data.pdf - Wikipedia

    en.wikipedia.org/wiki/File:Overfitting_on...

    English: This image represents the problem of overfitting in machine learning. The red dots represent training set data. The green line represents the true functional relationship, while the red line shows the learned function, which has fallen victim to overfitting.

  7. Validation and verification (medical devices) - Wikipedia

    en.wikipedia.org/wiki/Validation_and...

    To establish a reference range, the Clinical and Laboratory Standards Institute (CLSI) recommends testing at least 120 patient samples. In contrast, for the verification of a reference range, it is recommended to use a total of 40 samples, 20 from healthy men and 20 from healthy women, and the results should be compared to the published reference range.

  8. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Data covering the nonlinear relationships observed in a servo-amplifier circuit. Levels of various components as a function of other components are given. 167 Text Regression 1993 [161] [162] K. Ullrich UJIIndoorLoc-Mag Dataset Indoor localization database to test indoor positioning systems. Data is magnetic field based. Train and test splits ...

  9. scikit-multiflow - Wikipedia

    en.wikipedia.org/wiki/Scikit-multiflow

    The scikit-multiflow library is implemented under the open research principles and is currently distributed under the BSD 3-clause license. scikit-multiflow is mainly written in Python, and some core elements are written in Cython for performance. scikit-multiflow integrates with other Python libraries such as Matplotlib for plotting, scikit-learn for incremental learning methods [4 ...

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