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  2. Oversampling and undersampling in data analysis - Wikipedia

    en.wikipedia.org/wiki/Oversampling_and_under...

    To create a synthetic data point, take the vector between one of those k neighbors, and the current data point. Multiply this vector by a random number x which lies between 0, and 1. Add this to the current data point to create the new, synthetic data point. Many modifications and extensions have been made to the SMOTE method ever since its ...

  3. Multiple correspondence analysis - Wikipedia

    en.wikipedia.org/wiki/Multiple_correspondence...

    MCA is performed by applying the CA algorithm to either an indicator matrix (also called complete disjunctive table – CDT) or a Burt table formed from these variables. [citation needed] An indicator matrix is an individuals × variables matrix, where the rows represent individuals and the columns are dummy variables representing categories of the variables. [1]

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

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

    The datasets are classified, based on the licenses, as Open data and Non-Open data. The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are ported on open data portals. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are ...

  5. Dummy data - Wikipedia

    en.wikipedia.org/wiki/Dummy_data

    Dummy data can be used as a placeholder for both testing and operational purposes. For testing, dummy data can also be used as stubs or pad to avoid software testing issues by ensuring that all variables and data fields are occupied. In operational use, dummy data may be transmitted for OPSEC purposes. Dummy data must be rigorously evaluated ...

  6. JMP (statistical software) - Wikipedia

    en.wikipedia.org/wiki/JMP_(statistical_software)

    Version 13.0 was released in September 2016 and introduced various improvements to reporting, ease-of-use and its handling of large data sets in memory. [29] [30] Version 14.0 was released in March 2018; new functionality included a Projects file management tool alongside the ability to use your own images as markers on your graph. [31]

  7. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    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.

  8. Chi-square automatic interaction detection - Wikipedia

    en.wikipedia.org/wiki/Chi-square_automatic...

    Luchman, J.N.; CHAID: Stata module to conduct chi-square automated interaction detection, Available for free download, or type within Stata: ssc install chaid. Luchman, J.N.; CHAIDFOREST: Stata module to conduct random forest ensemble classification based on chi-square automated interaction detection (CHAID) as base learner , Available for free ...

  9. Anscombe's quartet - Wikipedia

    en.wikipedia.org/wiki/Anscombe's_quartet

    The four datasets composing Anscombe's quartet. All four sets have identical statistical parameters, but the graphs show them to be considerably different. Anscombe's quartet comprises four datasets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphed.