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Potential Density Initializer

KNIME Active Learning Plug-in version 4.3.0.v202011190949 by KNIME AG, Zurich, Switzerland

Creates a potential based density model of a n-dimensional vector space.
In order to calculate the density at a specific row in the table, its immediate neighborhood in the vector space is considered. The closer and more numerous the neighbors the higher is the density. The size of the neighborhood is controlled by the Radius Alpha parameter and a larger value will result in a larger neighborhood. For more details see the PBAC paper by Cebron et. al.
If the node fails due to memory problems, this is usually due to a too large radius Alpha which results in many rows with a lot of neighbors.


Column Selection
The columns that make up the vector space.
Radius Alpha
Radius Alpha parameter, influences the size of the neighborhood considered for the density estimation. A larger value corresponds to a larger neighborhood. Note that the node will issue a warning if any of the rows in the input table has more than 20% of the table in its neighborhood because this might indicate a too large Radius Alpha value.
Missing Value Handling
Missing values can't be used to build the model therefore two strategies exist to cope with missing values. The node can either fail if it encounters a missing value in one of the used columns or it can ignore the row in which the missing value occurred. Note that an ignored row is not part of the model and consequently the Density Scorer node will treat it as an unknown row.

Input Ports

Table to build a density model for.

Output Ports

Density scorer model.

Best Friends (Incoming)

Best Friends (Outgoing)

  • Active Learning Loop Start (100 %) 



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