HANA KNN

Properties that can be configured for the HANA KNN algorithm.

Syntax Use this component to classify objects based on the trained sample data. In KNN, objects are classified by the majority votes of its neighbors.
Note Creating models using the HANA KNN algorithm is not supported.
HANA KNN Properties
Table 1: Algorithm Properties
Property Description
Features Select input columns with which you want to perform the analysis.
Neighborhood Count Enter the number of neighbors to consider for finding distances. The default value is 5.
Voting Type Select the voting type for calculating neighborhood count.
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 the missing values.
Schema Name Enter the schema name that contains the trained data.
Table Name Enter the table name that contains the trained data.
Independent Columns Enter input columns, which you want to consider for training data.
Dependent Column Enter the output column that you want to consider for training data.
Predicted Column Name Enter a name for the new column that contains the classification values.
Number of Threads Enter the number of threads using which you want the algorithm to execute. The default value is 1.