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Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance. This method is widely used for normalization in many machine learning algorithms (e.g., support vector machines, logistic regression, and artificial neural networks).

Standardization involves transforming the features such that they have a mean of zero and a standard deviation of one. This is done by subtracting the mean and dividing by the standard deviation of each feature. On the other hand, normalization scales the features to a fixed range, usually [0, 1].

The goal of standardization is to enforce a level of consistency or uniformity to certain practices or operations within the selected environment. An example of standardization would be the generally accepted accounting principles (GAAP) to which all companies listed on U.S. stock exchanges must adhere.

Documentation and standardization decrease the possibility of errors that may occur during complex processes. Likewise, if companies leave processes on individual discretion, it increases the chances of errors. The idea is to make processes easy to follow even for the most junior technician.

Standardization or standardisation is the process of implementing and developing technical standards based on the consensus of different parties that include firms, users, interest groups, standards organizations and governments.

Document standardization is not only limited to establishing and maintaining consistent formats and terminologies. It also refers to procedures for creating, editing, and storing documents. The goal is to ensure that all documents produced by an organization are clear, consistent, and easy to understand.

Is normalisation and standardisation same? Standardization is divided by the standard deviation after the mean has been subtracted. Data is transformed into a range between 0 and 1 by normalization, which involves dividing a vector by its length.