Abstract: A processing system including at least one processor may obtain a data set comprising a plurality of records, each record associating at least one feature value of at least one feature with a value of a target variable. The processing system may next segregate the plurality of records into a plurality of subsets based upon a range of values of the at least one feature and calculate a plurality of sub-volumes for the plurality of subsets, each sub-volume comprising a sum of the values of the target variable from records in a respective subset. The processing system may then generate a significance metric that is based on a difference between a highest sub-volume and a lowest sub-volume of the plurality of sub-volumes and select the at least one feature to train a classification model associated with the target variable, based upon the significance metric.

Abstract: Aspects of the subject disclosure may include, for example, dividing a feature range of a feature into a plurality of subsets that span the feature range, calculating an average target variable value for each subset of the plurality of subsets, resulting in a plurality of average target variable values, and estimating a measure of feature significance with respect to a target variable by determining a difference between a maximum average target variable value and a minimum average target variable value in the plurality of average target variable values. Other embodiments are disclosed.

Abstract: Improving configuration of routing areas within an area of interest can include obtaining input data including a list of common language location identifiers within a defined area of interest and identification of a number of routing areas to be created within the area of interest; creating a bounding rectangle around the area of interest represented by the list of common language location identifiers; generating, within the bounding rectangle, a uniformly distributed sequence point; determining, from within the list of common language location identifiers, if the uniformly distributed sequence point is located in a particular common language location identifier of the list of common language location identifiers; if so, adding, to a list of kernel common language location identifiers the uniformly distributed sequence point; and if not, discarding the uniformly distributed sequence point.

Abstract: Facilitating achievement of equilibrium between supply and demand of resources is provided herein. Operations of a system include determining a first amount of resources that are to satisfy first requests in a first area, and a second amount of resources that are to satisfy second requests in a second area and comparing the first amount of resources with a first quantity of available resources in the first area, and the second amount of resources with a second quantity of available resources in the second area. Further, based on the first quantity of available resources being less than the first amount of resources and the second quantity of available resources being more than the second amount of resources: classifying the first area as an overloaded condition, classifying the second area as an underloaded condition, and moving a resource of the second quantity of available resources to the first area.

Abstract: Facilitating selection of the most significant set of categorical features in machine learning is provided herein. Operations of a system include determining a list of unique values of a categorical variable. The operations also include calculating respective mean values, of a target variable, for unique values of the list of unique values of the categorical variable. Further, the operations include sorting the list of unique values by the respective mean values, resulting in a sorted list. The operations also include calculating respective derivatives of the respective mean values in the sorted list considering the respective mean values as a function and a number of the respective mean values in the sorted list as an independent variable. Additionally, the operations include determining a minimum derivative value over the sorted list and outputting the minimum derivative value as a resulting variable significance value.