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  2. Relation (database) - Wikipedia

    en.wikipedia.org/wiki/Relation_(database)

    Relation, tuple, and attribute represented as table, row, and column respectively. In database theory, a relation, as originally defined by E. F. Codd, [1] is a set of tuples (d 1,d 2,...,d n), where each element d j is a member of D j, a data domain. Codd's original definition notwithstanding, and contrary to the usual definition in ...

  3. Tuple relational calculus - Wikipedia

    en.wikipedia.org/wiki/Tuple_relational_calculus

    (Note that this is a simplification from the full relational model where there is more than one domain and a header is not just a set of column names but also maps these column names to a domain.) Given a domain D we define a tuple over D as a partial function that maps some column names to an atomic value in D. An example would be (name ...

  4. Data orientation - Wikipedia

    en.wikipedia.org/wiki/Data_orientation

    The two most common representations are column-oriented (columnar format) and row-oriented (row format). [ 1 ] [ 2 ] The choice of data orientation is a trade-off and an architectural decision in databases , query engines, and numerical simulations. [ 1 ]

  5. Tuple - Wikipedia

    en.wikipedia.org/wiki/Tuple

    A 1‑tuple is called a single (or singleton), a 2‑tuple is called an ordered pair or couple, and a 3‑tuple is called a triple (or triplet). The number n can be any nonnegative integer . For example, a complex number can be represented as a 2‑tuple of reals, a quaternion can be represented as a 4‑tuple, an octonion can be represented as ...

  6. Cartesian product - Wikipedia

    en.wikipedia.org/wiki/Cartesian_product

    If the Cartesian product rows × columns is taken, the cells of the table contain ordered pairs of the form (row value, column value). [ 4 ] One can similarly define the Cartesian product of n sets, also known as an n -fold Cartesian product , which can be represented by an n -dimensional array, where each element is an n - tuple .

  7. CatBoost - Wikipedia

    en.wikipedia.org/wiki/Catboost

    It works on Linux, Windows, macOS, and is available in Python, [8] R, [9] and models built using CatBoost can be used for predictions in C++, Java, [10] C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub. [6] InfoWorld magazine awarded the library "The best machine learning tools" in 2017.

  8. Boosting (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Boosting_(machine_learning)

    scikit-learn, an open source machine learning library for Python; Orange, a free data mining software suite, module Orange.ensemble; Weka is a machine learning set of tools that offers variate implementations of boosting algorithms like AdaBoost and LogitBoost

  9. LPBoost - Wikipedia

    en.wikipedia.org/wiki/LPBoost

    The soft margin is the size of the margin separating positive from negative training instances minus positive slack variables that carry penalties for margin-violating samples. Thus, the soft margin may be positive although not all samples are linearly separated by the classification function.