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  2. Dummy variable (statistics) - Wikipedia

    en.wikipedia.org/wiki/Dummy_variable_(statistics)

    For example, in econometric time series analysis, dummy variables may be used to indicate the occurrence of wars, or major strikes. It could thus be thought of as a Boolean, i.e., a truth value represented as the numerical value 0 or 1 (as is sometimes done in computer programming). Dummy variables may be extended to more complex cases.

  3. 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 ...

  4. 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 ...

  5. 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.

  6. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    The goal is to create a model that predicts the value of a target variable based on several input variables. A decision tree is a simple representation for classifying examples. For this section, assume that all of the input features have finite discrete domains, and there is a single target feature called the "classification".

  7. SAS (software) - Wikipedia

    en.wikipedia.org/wiki/SAS_(software)

    Data sets are organized into tables with rows called "observations" and columns called "variables". Additionally, each piece of data has a descriptor and a value. [4] [7] PROC statements call upon named procedures. Procedures perform analysis and reporting on data sets to produce statistics, analyses, and graphics.

  8. Multinomial logistic regression - Wikipedia

    en.wikipedia.org/wiki/Multinomial_logistic...

    The value of the actual variable is then determined in a non-random fashion from these latent variables (i.e. the randomness has been moved from the observed outcomes into the latent variables), where outcome k is chosen if and only if the associated utility (the value of ,) is greater than the utilities of all the other choices, i.e. if the ...

  9. List of datasets in computer vision and image processing

    en.wikipedia.org/wiki/List_of_datasets_in...

    RAWPED is a dataset for detection of pedestrians in the context of railways. The dataset is labeled box-wise. 26000 Images Object recognition and classification 2020 [90] [91] Tugce Toprak, Burak Belenlioglu, Burak Aydın, Cuneyt Guzelis, M. Alper Selver OSDaR23 OSDaR23 is a multi-sensory dataset for detection of objects in the context of railways.