HANA Weighted Score Analysis

Properties that can be configured for the HANA Weighted Score Analysis algorithm.

Syntax

A weighted score table is a method for evaluating alternatives when the importance of each criterion differs. In a weighted score table, each alternative is given a score for each criterion. These scores are then weighted by the importance of each criterion. All of an alternative's weighted scores are then added together to calculate its total weighted score. The alternative with the highest total score should be the best alternative.

You can use weighted score tables to make predictions about future customer behavior. You first create a model based on historical data in the data mining application, and then apply the model to new data to make the prediction. The prediction, that is, the output of the model, is called a score. You can create a single score for your customers by taking into account different dimensions.

A function defined by weighted score tables is a linear combination of functions of a variable.

f(x1,…,xn) = w1 × f1(x1) + … + wn × fn(xn)

HANA Weighted Score Analysis
Table 1: Algorithm Properties
Property Description
Column Name Select the input column with which you want to perform the analysis.
Type Select the type as "Discrete" if the selected column has categorical data or select the type as "Continuous" if the selected column has numerical data.
Weights Enter the weigths for the selected column. The default value is 0.0.
Keys and Scores Enter the values for keys and scores.
Missing Values Select the method for handling missing values.
  • Ignore: The algorithm skips the records containing missing values in features or target variables.
  • Keep: The algorithm retains missing values.
Number of Threads Enter the number of threads using which the algorithm should execute. The default value is 1.
Predicted Column Name Enter a name for the new column that contains the predicted values.