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In some programming languages, eval, short for evaluate, is a function which evaluates a string as though it were an expression in the language, and returns a result; in others, it executes multiple lines of code as though they had been included instead of the line including the eval.
Python supports normal floating point numbers, which are created when a dot is used in a literal (e.g. 1.1), when an integer and a floating point number are used in an expression, or as a result of some mathematical operations ("true division" via the / operator, or exponentiation with a negative exponent).
In a programming language, an evaluation strategy is a set of rules for evaluating expressions. [1] The term is often used to refer to the more specific notion of a parameter-passing strategy [2] that defines the kind of value that is passed to the function for each parameter (the binding strategy) [3] and whether to evaluate the parameters of a function call, and if so in what order (the ...
Lazy evaluation is difficult to combine with imperative features such as exception handling and input/output, because the order of operations becomes indeterminate. The opposite of lazy evaluation is eager evaluation, sometimes known as strict evaluation. Eager evaluation is the evaluation strategy employed in most [quantify] programming languages.
The relationship between sensitivity and specificity, as well as the performance of the classifier, can be visualized and studied using the Receiver Operating Characteristic (ROC) curve. In theory, sensitivity and specificity are independent in the sense that it is possible to achieve 100% in both (such as in the red/blue ball example given above).
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
Furthermore, the mean square difference between a model and the data can be calculated by adding in quadrature the bias and the standard deviation of the errors. The code for these "modified" Taylor diagrams was developed, and is available in, Python [13].
It is also possible to identify the types of difference by looking at an (,) plot. Quantity difference exists when the average of the X values does not equal the average of the Y values. Allocation difference exists if and only if points reside on both sides of the identity line. [4] [5]