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Tag: rapidminer User: Sebastian Loh Licence: by-nd

Workflow Change Class Distribution of Your Training... (1)

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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 propert...

Created: 2011-01-21 | Last updated: 2011-01-21

Workflow 2. Getting Started: Retrieve and Apply a M... (1)

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This getting started process demonstrates how to load (retrieve) a model from the repository and apply it to a data set. The result is a data set (at the lab output for "labeled data" ) with has a new "prediction" attribute which indicated the prediction for each example (ie. row/record). You will need to adjust the path of the retrieve data operator to the actual location where the model is stored by a previews execution of the "1. Getting Started: Learn and Store a...

Created: 2011-01-17 | Last updated: 2011-01-19

Workflow 1. Getting Started: Learn and Store a Model (1)

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This getting started process shows the first step of learning and storing a model. After a model is learned, you can load (Retrieve operator) the model and apply it to a test data set (see 2. Getting Started: Retrieve and Apply Model). The process is NOT concerned with evaluation of the model. This process will not immediately run in RapidMiner because you have to adjust the repository path in the Retrieve operator. Tags: Rapidminer, model, learn, learning, training, train, store, first step

Created: 2011-01-17 | Last updated: 2011-01-17

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