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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]
If cross-validation is used to decide which features to use, an inner cross-validation to carry out the feature selection on every training set must be performed. [30] Performing mean-centering, rescaling, dimensionality reduction, outlier removal or any other data-dependent preprocessing using the entire data set.
Conditional quantifiers are meant to capture certain properties concerning conditional reasoning at an abstract level. Generally, it is intended to clarify the role of conditionals in a first-order language as they relate to other connectives, such as conjunction or disjunction. While they can cover nested conditionals, the greater complexity ...
nested blocks of imperative source code such as nested if-clauses, while-clauses, repeat-until clauses etc. information hiding: nested function definitions with lexical scope; nested data structures such as records, objects, classes, etc. nested virtualization, also called recursive virtualization: running a virtual machine inside another ...
Two nested single-condition IFs, or one IF with two conditions, would produce a complexity of 3. Another way to define the cyclomatic complexity of a program is to look at its control-flow graph , a directed graph containing the basic blocks of the program, with an edge between two basic blocks if control may pass from the first to the second.
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While not itself a conditional function, it is often used inside of those functions, so it is briefly described here. See Manual:Expr parser function syntax for further details. {{#expr: expression}} Unlike the #if function, all values in the expression evaluated by #expr are assumed to be numerical. It does not work with arbitrary strings.
In statistics, model validation is the task of evaluating whether a chosen statistical model is appropriate or not. Oftentimes in statistical inference, inferences from models that appear to fit their data may be flukes, resulting in a misunderstanding by researchers of the actual relevance of their model.