![]() ![]() If the X value and the Y value were both below average, then the product above would be of two negative numbers, which would also be positive. Then the product (X(i)-X').(Y(i)-Y') would be the product of two positive numbers which would be positive. Suppose that an X value was above average, and that the associated Y value was also above average. ![]() There can be other formulas and definitions but let us stick to this one for simplicity.Īs discussed earlier a positive value for the correlation implies a positive association. The correlation is computed as summation from 1 to n of the product (X(i)-X').(Y(i)-Y') and then dividing this summation by the product (n-1).S(X).S(Y) where n is the total number of examples and i is the increment variable of summation. Suppose we have two attributes X and Y, with means X' and Y' and standard deviations S(X) and S(Y) respectively. In this case large values of X tend to be associated with small values of Y and vice versa. A negative value for the correlation implies a negative or inverse association. In this case large values of X tend to be associated with large values of Y and small values of X tend to be associated with small values of Y. A positive value for the correlation implies a positive association. Binominal labels work because of the representation as 0 and 1, as do numerical ones.Ī correlation is a number between -1 and +1 that measures the degree of association between two attributes (call them X and Y). It cannot be applied on Polynominal attributes because the polynominal classes provide no information about their ordering, therefore the weights are more or less random depending on the internal numerical representation of the classes. Please note that the Weight by Correlation operator can be applied only on ExampleSets with numerical or binominal label. The higher the weight of an attribute, the more relevant it is considered. The Weight by Correlation operator calculates the weight of attributes with respect to the label attribute by using correlation. This weighting scheme is based upon correlation and it returns the absolute or squared value of correlation as attribute weight. SynopsisThis operator calculates the relevance of the attributes by computing the value of correlation for each attribute of the input ExampleSet with respect to the label attribute. You are viewing the RapidMiner Studio documentation for version 9.6 - Check here for latest version Weight by Correlation
0 Comments
Leave a Reply. |