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A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
For instance, assuming that the get_data() function gets data from the Internet, this Python code is insecure: session [ 'authenticated' ] = False data = get_data () foo = eval ( data ) An attacker could supply the program with the string "session.update(authenticated=True)" as data, which would update the session dictionary to set an ...
Introduced in Python 2.2 as an optional feature and finalized in version 2.3, generators are Python's mechanism for lazy evaluation of a function that would otherwise return a space-prohibitive or computationally intensive list. This is an example to lazily generate the prime numbers:
Python. The use of the triple-quotes to comment-out lines of source, does not actually form a comment. [19] The enclosed text becomes a string literal, which Python usually ignores (except when it is the first statement in the body of a module, class or function; see docstring). Elixir
In programming language theory, lazy evaluation, or call-by-need, [1] is an evaluation strategy which delays the evaluation of an expression until its value is needed (non-strict evaluation) and which also avoids repeated evaluations (by the use of sharing). [2] [3] The benefits of lazy evaluation include:
The read function accepts an expression from the user, and parses it into a data structure in memory. For instance, the user may enter the s-expression (+ 1 2 3), which is parsed into a linked list containing four data elements. The eval function takes this internal data structure and evaluates it. In Lisp, evaluating an s-expression beginning ...
Manhattan distance, also known as Taxicab geometry, is a commonly used similarity measure in clustering techniques that work with continuous data. It is a measure of the distance between two data points in a high-dimensional space, calculated as the sum of the absolute differences between the corresponding coordinates of the two points | | + | |.
In mathematics and computer science, Horner's method (or Horner's scheme) is an algorithm for polynomial evaluation.Although named after William George Horner, this method is much older, as it has been attributed to Joseph-Louis Lagrange by Horner himself, and can be traced back many hundreds of years to Chinese and Persian mathematicians. [1]