Weka& Rapid Miner Tutorial Clustering Example showing simple Kmeans on the Iris dataset Talk about hacking weka discretization cross validations Simple CLI provides a commandline interface to wekas routines Explorer interface provides a graphical front end to wekas routines and components Experimenter allows you to build What is the default discretization tool used by WEKA?
Many MC learning algorithms perform discretization of continuous data before performing a feature selection operation. WEKA uses discretization by default. The WEKA discretization filter, can divide the ranges blindly, or used various statistical techniques to automatically determine the best way of partitioning the data.
In attributes an array containing indexes of attributes to Discretize. Since the array will typically come from a program, attributes are indexed from 0. Since the array will typically come from a program, attributes are indexed from 0. weka manual for Command Line Interface. Dear friends, I Have used the Weka Discretization Filter through the Explorer Interface and I would likle to tune the parameters also with the command Manual Discretization This post was updated on.
Hi, I am using unsupervised discretization (currently 3 bin equal width) on my dataset before I feed it into a classifier. The Weka manual (Weka, Weka, Weka ), as included in the distribution.
The Weka API, extracted from the Javadoc ( Weka 3. 6, Weka 3. 8, Weka 3. 9 ), as included in the distribution. A list of packages for Weka 3. 7 that can be installed via its package manager.
Well let me clear the problem that i am facing, I have data set with two classes' values (good, bad). I am trying to get imbalanced class values. Therefore i separate the data set into two sets (one includes the good instances and one bad instances).
When i do the discretization before and i merge the two sets, the results is satisfactory but if i do it afterward it is not that good.