hasercommunity.blogg.se - Weighted mean divided by weighted standard deviation

sort_directionThis parameter is only available when the sort weights parameter is set to true.

If this parameter is set to true, the order of the sorting is specified using the sort direction parameter.

sort_weightsThis parameter indicates if the attributes should be sorted according to their weights in the results.

If set to true, all weights are normalized in the range from 0 to 1.

normalize_weightsThis parameter indicates if the calculated weights should be normalized or not.

This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace. The ExampleSet that was given as input is passed without changing to the output through this port. The attributes with higher weight are considered more relevant. This port delivers the weights of the attributes with respect to the label attribute.

It is output of the Retrieve operator in the attached Example Process. The formula is simple: it is the square root of the Variance. The standard deviation is a measure of how spread out numbers are.

A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data points are spread out over a large range of values. Standard deviation shows how much variation or dispersion exists from the average (mean, or expected value). Please note that this operator can be only applied on ExampleSets with numerical label. The standard deviations can be normalized by average, minimum, or maximum of the attribute. The higher the weight of an attribute, the more relevant it is considered. The Weight by Deviation operator calculates the weight of attributes with respect to the label attribute based on the (normalized) standard deviation of the attributes.

SynopsisThis operator calculates the relevance of attributes of the given ExampleSet based on the (normalized) standard deviation of the attributes.