Package

weka.filters.supervised.instance

Synopsis

Produces a random subsample of a dataset. The original dataset must fit entirely in memory. This filter allows you to specify the maximum "spread" between the rarest and most common class. For example, you may specify that there be at most a 2:1 difference in class frequencies. When used in batch mode, subsequent batches are NOT resampled.

Options

The table below describes the options available for SpreadSubsample.

Option	Description
adjustWeights	Wether instance weights will be adjusted to maintain total weight per class.
distributionSpread	The maximum class distribution spread. (0 = no maximum spread, 1 = uniform distribution, 10 = allow at most a 10:1 ratio between the classes).
maxCount	The maximum count for any class value (0 = unlimited).
randomSeed	Sets the random number seed for subsampling.

Capabilities

The table below describes the capabilites of SpreadSubsample.

Capability	Supported
Class	Nominal class, Binary class
Attributes	Binary attributes, Missing values, Nominal attributes, Numeric attributes, Unary attributes, String attributes, Empty nominal attributes, Relational attributes, Date attributes
Min # of instances	0