HANA Logistic Regression

Properties that can be configured for the HANA Logistic Regression algorithm.

Syntax Use this algorithm when the independent variables are categorical, or a mix of continuous and categorical values. Logistic Regression is a prediction approach similar to Ordinary Least Square (OLS) regression.
Note The data type of columns used during model scoring should be same as the data type of columns used while building the model.
HANA Logistic Regression properties
Table 1: Algorithm Properties
Property Description
Output Mode Select the mode in which you want to use the output of this algorithm.
Possible values:
  • Trend: Predicts the values for the dependent column and adds an extra column in the output containing the predicted values.
  • Fill: Fills missing values in the target column.
Independent Columns Select the input columns with which you want to perform the regression analysis.
Dependent Column Select the target column for which you want to perform the regression analysis.
Iteration Method Select the iteration method.
Missing Values Select the method for handling missing values.
Possible methods:
  • Ignore: The algorithm skips the records containing missing values in the independent or dependent columns.
  • Keep: The algorithm retains the records containing missing values during calculation.
Show Fitted Values Select this option to view the fitted values in a new column.
Predicted Column Name Enter a name for the newly-created column that contains the predicted values.
Maximum iteration Enter the maximum number of iterations allowed to calculate the algorithm coefficient. The default value is 100.
Exit Threshold Enter the threshold value for exiting from the iterations. The default value is 0.00001.
Number of Threads Enter the number of threads that the algorithm should use during execution. The default value is 4.
Mapping Value for 0 Enter a value for a variable, which is mapped to 0.
Mapping Value for 1 Enter a value for a variable, which is mapped to 1.