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Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.
a := (1 + 2) * 5. To a human, this seems a fairly simple and obvious calculation ("one plus two is three, times five is fifteen"). However, the low-level steps necessary to carry out this evaluation, and return the value "15", and then assign that value to the variable "a", are actually quite subtle and complex.
In computer science, syntactic sugar is syntax within a programming language that is designed to make things easier to read or to express. It makes the language "sweeter" for human use: things can be expressed more clearly, more concisely, or in an alternative style that some may prefer.
[1] Many languages that apply this style attempt to minimize or eliminate side effects by describing what the program must accomplish in terms of the problem domain, rather than describing how to accomplish it as a sequence of the programming language primitives [2] (the how being left up to the language's implementation).
For tie-breaking, Python 3 uses round to even: round(1.5) and round(2.5) both produce 2. [123] Versions before 3 used round-away-from-zero: round(0.5) is 1.0, round(-0.5) is −1.0. [124] Python allows Boolean expressions with multiple equality relations in a manner that is consistent with general use in mathematics.
In computer science, divide and conquer is an algorithm design paradigm.A divide-and-conquer algorithm recursively breaks down a problem into two or more sub-problems of the same or related type, until these become simple enough to be solved directly.
The boolean values True and False were added to the language in Python 2.2.1 as constants (subclassed from 1 and 0) and were changed to be full blown keywords in Python 3. The binary comparison operators such as == and > return either True or False.
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...