Change Class Distribution of Your Training Data Set by Filtering and Sampling / Normalize Class Distribution / Stratification

Created: 2011-01-21 14:57:11      Last updated: 2011-01-21 14:57:12

This example process shows how to change the class distribution of your training data set (in this case the training data is what ever comes out of the "myData reader").

The given training set has a distribution of 10 "Iris-setosa" examples, 40 "Iris-versicolor" examples and 50 "Iris-virginica" examples. The aim is to get a data set which has the class distribution for the label, lets say 10 "Iris-setosa", 20 "Iris-versicolor" and 20 "Iris-virginica.

Beware that this may change some properties of the data so that a model trained on this subset but applied to a set of the initial structure may be biased.

See also the "Same Number of Examples per Class" process here on myExperiment http://www.myexperiment.org/workflows/1315.html

Tags: Rapidminer, sample, filter, class distribution, normalization, data set, label, label distribution, training, training data, Stratification

Information Preview

Information Run

Not available


Information Workflow Components

Unavailable

Information Workflow Type

RapidMiner

Information Uploader

Information License

All versions of this Workflow are licensed under:

Information Version 1 (of 1)

Information Credits (0)

(People/Groups)

None

Information Attributions (0)

(Workflows/Files)

None

Information Tags (11)

Log in to add Tags

Information Shared with Groups (0)

None

Information Featured In Packs (0)

None

Log in to add to one of your Packs

Information Attributed By (0)

(Workflows/Files)

None

Information Favourited By (0)

No one

Information Statistics

 

Citations (0)

None


Version History


Reviews Reviews (0)

No reviews yet

Be the first to review!



Comments Comments (0)

No comments yet

Log in to make a comment




Workflow Other workflows that use similar services (0)

There are no workflows in myExperiment that use similar services to this Workflow.