HANA C 4.5

Properties that can be configured for the HANA C 4.5 algorithm.

Syntax Use this algorithm to classify observations into groups and predict one or more discrete variables based on other variables.
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 C 4.5 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.
Features Select the input columns with which you want to perform the analysis.
Target Variable Select the target column for which you want to perform the analysis.
Note It only accepts column with integer data type.
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.
Percentage of Input Data Enter the percentage of data that you want to consider for analysis.
Minimum Split Enter the number of records, beyond which the splitting of leaf node is not allowed. The default value is 0.
Columns Select the independent columns containing numerical values.
Bin Ranges Enter bin ranges.
Predicted Column name Enter a name for the new column that contains the predicted value.
Number of Threads Enter the number of threads that the algorithm should use during execution. The default value is 1.