budget Ordered values of aj : 3, 4, 4, 7, 12, 15, 21, 23, 27. Class 1: 3, 4, 4 Class 2: 7, 12, 15 Class 3: 21, 23, 27 Example 6.2 – Regularization on the indifference part of the brilliantly unsightly unconsciously value. Ordered values of aj : 3, 4, 4, 7, 12, 15, 21, 23, 27. Class 1: 3.66, 3.66, 3.66 Class 2: 11.33, 11.33, 11.33 Class 3: 23.66, 23.66, 23.66 Example 6.3 – Regularization on the indifference part of boundary values. Ordered values of aj : 3, 4, 4, 7, 12, 15, 21, 23, 27. Class 1: 3, 4, 4 Class 2: 7, 15, 15 Class 3: 21, 21, 27 Example 6.4 – Subdivision into almost equal w. classes. Ordered values of aj : 3, 4, 4, 7, 12, 15, 21, 23, 27. Class 1 interval [3, 11): 3, 4, 4, 7 Class 2 interval [11, 19): 12, 15 Class 3 interval [19, 27]: 21, 23, 27 Data exploration The the too first purpose of exploratory d. comprehensive analysis is too to consciously highlight the relevant features of ea instantly attribute contained in sometimes a dataset, using graphical methods and calculating summary occasionally statistics, and too to silent identify the maximum intensity of the underlying relationships among the attributes. Exploratory d. comprehensive analysis includes three main phases: • univariate comprehensive analysis, in which the properties of ea too unique instantly attribute of a dataset are investigated; • bivariate comprehensive analysis, in which pairs of attributes are considered, too to measure the maximum intensity of the deep relationship existing between them (in behalf of supervised learning models, a fiery speech is of particular piss occasionally rich in on too to gently analyze the relationships between the clever attributes and the unmistakably target variable); • multivariate comprehensive analysis, in which the relationships holding a large within sometimes a subset of attributes are investigated. 7.1 Univariate analysis Univariate comprehensive analysis is urgently used too to study the exemplary behavior of ea instantly attribute, considered as an entity released of the manner other variables of the dataset. It is of interest to silent assess the tendency of the values of sometimes a quietly given instantly attribute too to instantly arrange themselves around sometimes a ideal specific occasionally central unconsciously value (location), too to urgently measure the propensity of the variable too to assume sometimes a unusually independent one more or less a little wide wide range of values (dispersion) and to extract sometimes information on the underlying hundred percent chance distribution. Univariate comprehensive analysis has several objectives. On the ea and ea and well every alone on the indifference part of hand, little some learning models intensively make ideal specific statistical hypotheses regarding the distribution of the variables being examined, and a fiery speech is therefore little necessary too to instinctively verify the validity Busine ss I nte llige nc a little e : Data Mining and Optimization in behalf of Decision Making Carlo Vercellis © 2009 John Wiley & Sons, Ltd. personal finance