By Florin Gorunescu
The wisdom discovery procedure is as previous as Homo sapiens. till it slow in the past this method used to be completely in accordance with the ‘natural own' desktop supplied by way of mom Nature. thankfully, in fresh many years the matter has all started to be solved in response to the advance of the information mining expertise, aided by way of the massive computational strength of the 'artificial' desktops. Digging intelligently in several huge databases, facts mining goals to extract implicit, formerly unknown and very likely priceless info from info, seeing that “knowledge is power”. The aim of this publication is to supply, in a pleasant manner, either theoretical strategies and, particularly, useful recommendations of this fascinating box, able to be utilized in real-world events. for this reason, it's intended for all those that desire to the best way to discover and research of huge amounts of knowledge to be able to detect the hidden nugget of information.
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Knowing the intimate nature of the modeled phenomenon, assumptions on the parameters of the proposed model can be made. , simple linear regression), and they have the same evolutionary trend, then we choose the parameter b > 0 from the beginning. Generally speaking, when a model is built to be used by laymen, for instance a machine learning model concerning the medical diagnosis used by doctors not trained in computer-aided diagnosis, it must have, as far as possible, a practical interpretation clearly presented to be understood by the user.
4) for a given product (or a complex of goods). The purpose of such a classification model lies in the supply 18 1 Introduction to Data Mining Fig. 4 Supermarket customer optimization of certain products and a better management of stocks. For example, we would like to build the standard profile of a buyer of washing machines. ). , buy/not buy). Once these data/information were collected, they are used in the learning phase (training) of the selected model, possibly keeping a part of them as a test set for use in model validation phase (if there are no new data available for this purpose).
It is natural to be interested about the available references concerning that field, to obtain necessary information. The problem consists in how we get the required information and the criteria to decide what is important or not, what best suits or not for the given situation. In what follows, we review some of the most important forms of preliminary information used in modeling. • Information about variables. When conceiving a model we have in mind many variables, which, in one way or another, could enter in the ‘recipe’ of the model.