Search results
Results from the WOW.Com Content Network
Laravel 3 was released in February 2012 with a set of new features including the cmd command-line interface (CLI) named Artisan, built-in support for more database management systems, database migrations as a form of version control for database layouts, support for handling events, and a packaging system called Bundles. An increase of Laravel ...
Test-driven development (TDD) is a way of writing code that involves writing an automated unit-level test case that fails, then writing just enough code to make the test pass, then refactoring both the test code and the production code, then repeating with another new test case.
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]
Verification and validation - Wikipedia
M&S Validation is the process of determining the degree to which a model, simulation, or federation of models and simulations, and their associated data are accurate representations of the real world from the perspective of the intended use(s). [3]
Leave-one-out cross-validation (LOOCV) is a particular case of leave-p-out cross-validation with p = 1. The process looks similar to jackknife; however, with cross-validation one computes a statistic on the left-out sample(s), while with jackknifing one computes a statistic from the kept samples only.
Web development is the work involved in developing a website for the Internet (World Wide Web) or an intranet (a private network). [1] Web development can range from developing a simple single static page of plain text to complex web applications, electronic businesses, and social network services.
One approach that is commonly used is to have the model builders determine validity of the model through a series of tests. [3] Naylor and Finger [1967] formulated a three-step approach to model validation that has been widely followed: [1] Step 1. Build a model that has high face validity. Step 2. Validate model assumptions. Step 3.