MLcomp version alpha Statistics: 1215 users, 402 datasets, 328 programs, 9938 runs (0 queued, 0 running), 0 workers

Logistic_weka_nominal

This programs is part of the WEKA classifier library. The code used to generate this program is from the java class 'weka/classifiers/functions/Logistic.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:

---------------------
Class for building and using a multinomial logistic regression model with a ridge estimator. There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992): If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix. The probability for class j with the exception of the last class is Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The last class has probability 1-(sum[j=1..(k-1)]Pj(Xi)) = 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The (negative) multinomial log-likelihood is thus: L = -sum[i=1..n]{ sum[j=1..(k-1)](Yij * ln(Pj(Xi))) +(1 - (sum[j=1..(k-1)]Yij)) * ln(1 - sum[j=1..(k-1)]Pj(Xi)) } + ridge * (B^2) In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables. Note that before we use the optimization procedure, we 'squeeze' the matrix B into a m*(k-1) vector. For details of the optimization procedure, please check weka.core.Optimization class. Although original Logistic Regression does not deal with instance weights, we modify the algorithm a little bit to handle the instance weights. For more information see: le Cessie, S., van Houwelingen, J.C. (1992). Ridge Estimators in Logistic Regression. Applied Statistics. 41(1):191-201. Note: Missing values are replaced using a ReplaceMissingValuesFilter, and nominal attributes are transformed into numeric attributes using a NominalToBinaryFilter.
---------------------

NOTE: This algorithm has no parameter tuning, it is using the default WEKA parameters

NOTE: WEKA's Classifiers read a data in the .arff format. For Multiclass datasets, the SVMlight format converted to .arff multiclass format so they can be read by WEKA programs

Task:	MulticlassClassification
User:	internal
Size:	18M
Status:	checked
Access:	open
Download:	Login required!