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  2. List of analyses of categorical data - Wikipedia

    en.wikipedia.org/wiki/List_of_analyses_of...

    This is a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables. General tests [ edit ]

  3. Comparison of data-serialization formats - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_data...

    UTF-8-encoded, preceded by varint-encoded integer length of string in bytes Repeated value with the same tag or, for varint-encoded integers only, values packed contiguously and prefixed by tag and total byte length — Smile \x21

  4. One-hot - Wikipedia

    en.wikipedia.org/wiki/One-hot

    Another downside of one-hot encoding is that it causes multicollinearity between the individual variables, which potentially reduces the model's accuracy. [citation needed] Also, if the categorical variable is an output variable, you may want to convert the values back into a categorical form in order to present them in your application. [10]

  5. Dummy variable (statistics) - Wikipedia

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

    Dummy variables are commonly used in regression analysis to represent categorical variables that have more than two levels, such as education level or occupation. In this case, multiple dummy variables would be created to represent each level of the variable, and only one dummy variable would take on a value of 1 for each observation.

  6. Autoencoder - Wikipedia

    en.wikipedia.org/wiki/Autoencoder

    Schematic structure of an autoencoder with 3 fully connected hidden layers. The code (z, or h for reference in the text) is the most internal layer. Autoencoders are often trained with a single-layer encoder and a single-layer decoder, but using many-layered (deep) encoders and decoders offers many advantages. [2]

  7. Statistical data type - Wikipedia

    en.wikipedia.org/wiki/Statistical_data_type

    This is a particular case of a Bayes network and often used for very long sequences, e.g. gene sequences or lengthy text documents. A number of models are specifically designed for such sequences, e.g. hidden Markov models. Random processes. These are similar to random sequences, but where the length of the sequence is indefinite or infinite ...

  8. Binary-to-text encoding - Wikipedia

    en.wikipedia.org/wiki/Binary-to-text_encoding

    A binary-to-text encoding is encoding of data in plain text. More precisely, it is an encoding of binary data in a sequence of printable characters . These encodings are necessary for transmission of data when the communication channel does not allow binary data (such as email or NNTP ) or is not 8-bit clean .

  9. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    IWE combines Word2vec with a semantic dictionary mapping technique to tackle the major challenges of information extraction from clinical texts, which include ambiguity of free text narrative style, lexical variations, use of ungrammatical and telegraphic phases, arbitrary ordering of words, and frequent appearance of abbreviations and acronyms ...