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  2. Lix (readability test) - Wikipedia

    en.wikipedia.org/wiki/Lix_(readability_test)

    LIX (abbreviation of Swedish läsbarhetsindex, "readibility index") is a readability measure indicating the difficulty of reading a text [1] developed by Swedish scholar Carl-Hugo Björnsson. It is defined as a sum of two numbers: the average sentence length and the percentage of words of more than six letters.

  3. Coleman–Liau index - Wikipedia

    en.wikipedia.org/wiki/Coleman–Liau_index

    The Coleman–Liau index is a readability test designed by Meri Coleman and T. L. Liau to gauge the understandability of a text. Like the Flesch–Kincaid Grade Level, Gunning fog index, SMOG index, and Automated Readability Index, its output approximates the U.S. grade level thought necessary to comprehend the text.

  4. Automated readability index - Wikipedia

    en.wikipedia.org/wiki/Automated_readability_index

    The automated readability index (ARI) is a readability test for English texts, designed to gauge the understandability of a text. Like the Flesch–Kincaid grade level, Gunning fog index, SMOG index, Fry readability formula, and Coleman–Liau index, it produces an approximate representation of the US grade level needed to comprehend the text.

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  6. Index (statistics) - Wikipedia

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

    In statistics and research design, an index is a composite statistic – a measure of changes in a representative group of individual data points, or in other words, a compound measure that aggregates multiple indicators. [1] [2] Indices – also known as indexes and composite indicators – summarize and rank specific observations. [2]

  7. Test score - Wikipedia

    en.wikipedia.org/wiki/Test_score

    A test score is a piece of information, usually a number, that conveys the performance of an examinee on a test. One formal definition is that it is "a summary of the evidence contained in an examinee's responses to the items of a test that are related to the construct or constructs being measured."

  8. Loss functions for classification - Wikipedia

    en.wikipedia.org/wiki/Loss_functions_for...

    In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy of predictions in classification problems (problems of identifying which category a particular observation belongs to). [1]

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    AOL Mail welcomes Verizon customers to our safe and delightful email experience!

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