EMImputation
Package
weka.filters.unsupervised.attribute
Synopsis
Replaces missing numeric values using Expectation Maximization with a multivariate normal model. Described in " Schafer, J.L. Analysis of Incomplete Multivariate Data, New York: Chapman and Hall, 1997."
Available in Weka 3.7.0 - 3.7.1. Available via the package management system for Weka >= 3.7.2 (EMImputation)
Options
The table below describes the options available for EMImputation.
Option |
Description |
|---|---|
debug |
Turns on output of debugging information. |
logLikelihoodThreshold |
Log-likelihood threshold for convergence in Expectation Maximization. If the change in the observed data log-likelihood across iterations is no more than this value, then convergence is considered to be achieved and the iterative process is ceased. (default = 0.0001) |
numIterations |
Maximum number of iterations for Expectation Maximization. EM is used to initialize the parameters of the multivariate normal distribution. (-1 = no maximum) |
ridge |
Ridge parameter for ridge prior. |
useRidgePrior |
Use a ridge prior instead of noninformative prior. |
Capabilities
The table below describes the capabilites of EMImputation.
Capability |
Supported |
|---|---|
Class |
Empty nominal class, Unary class, Missing class values, String class, Relational class, Nominal class, Date class, No class, Numeric class, Binary class |
Attributes |
Missing values, Numeric attributes |
Min # of instances |
0 |