Workflows

Random recommender (1)

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This process does a random item recommendation; for a given item ID, from the example set of items, it randomly recommends a desired number of items. The purpose of this workflow is to produce a random recommendation baseline for comparison with different recommendation solutions, on different retrieval measures. The inputs to the process are context defined macros: %{id} defines an item ID for which we would like to obtain recommendation and %{recommender_no} defines the required number of ...

Created: 2011-03-15 | Last updated: 2011-03-15

RCOMM 2011 Challenge 2: Vodka or President? (1)

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This is a solution for Challenge 2 of the a live data mining process design competition "Who Wants to be a Data Miner" held at RCOMM 2011 in Dublin. Those of you who loved "You Don't Know Jack" will remember this task: To tell whether a certain word is the name of a vodka or the name of a leader of the Soviet Union. The RapidMiner process was allowed to download data from Wikipedia to make this decision. One input file contains a list of words for which two attributes "Vodka" or "Leader" wi...

Created: 2011-11-02

RCOMM Challenge 3: Fibonacci Numbers (Inte... (1)

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At the RComm 2010 (www.rcomm2010.org), an unusual competition was held. Titled "Who Wants to Be a Data Miner", three challenges were issued to the participants of the conference. In all challenges, participants had to design RapidMiner processes as quickly as possible. This is the original solution I had in mind for Challenge 2: "Fibonacci Numbers". It defines a macro n, recurses by applying itself using an "Embed Process" operator on n-1 and n-2, appends the results (so the length is F(n-1)...

Created: 2010-09-17 | Last updated: 2010-09-17

kddcup98 direct marketing (1)

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RapidMiner supports Meta Learning by embedding one or several basic learners as children into a parent meta learning operator. In this example we generate a data set with the ExampleSetGenerator operator and apply an improved version of Stacking on this data set. The Stacking operator contains four inner operators, the first one is the learner which should learn the stacked model from the predictions of the other four child operators (base learners). Other meta learning schemes like Boosting ...

Created: 2012-03-15

RCOMM Challenge 3: Fibonacci Numbers (Impr... (1)

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At the RComm 2010 (www.rcomm2010.org), an unusual competition was held. Titled "Who Wants to Be a Data Miner", three challenges were issued to the participants of the conference. In all challenges, participants had to design RapidMiner processes as quickly as possible. This is the winning process of Challenge 2: "Fibonacci Numbers" by Matko Bošnjak. This was the task: The n-th Fibonacci number is F(n)=F(n-1)+F(n-2), and F(0)=0, F(1)=1. Create a process that creates an example set with F(n)...

Created: 2010-09-17 | Last updated: 2010-09-17

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

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

Content based recommender system template (1)

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As an input, this workflow takes two distinct example sets: a complete set of items with IDs and appropriate textual attributes (item example set) and a set of IDs of items our user had interaction with (user example set). Also, a macro %{recommendation_no} is defined in the process context, as a required number of outputted recommendations. The first steps of the workflow are to preprocess those example sets; select only textual attributes of item example set, and set ID roles on both of th...

Created: 2011-05-05 | Last updated: 2011-05-09

Credits: Matko Bošnjak Ninoaf

Attributions: Datasets for the pack: RCOMM2011 recommender systems workflow templates

Crossvalidation with SVM (1)

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Performs a crossvalidation on a given data set with nominal label, using a Support Vector Machine as a learning algorithm. Inside the cross validation, the first subprocess generates an SVM model, and the second subprocess evaluates it. applying it on a so-far unused subset of the data and counting the misclassifications.

Created: 2010-04-29

Przykład metody Stacking (1)

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Poniższy przepÅ‚yw pokazuje wykorzystanie operatora Stacking do tworzenia meta-klasyfikatorów. Operator Stacking pozwala na zagnieżdżenie dowolnej liczby modeli bazowych, które bÄ™dÄ… równolegle uczone na zbiorze uczÄ…cym. Drugim operatorem zagnieżdżonym jest model klasyfikatora, który uczy siÄ™ na odpowiedziach modeli bazowych (czyli buduje model modeli odpowiedzi). W przykÅ‚adzie jako modele bazowe wykorzystano: drzewo decyzyjne, algorytm k-NN, sieć neurono...

Created: 2011-05-25 | Last updated: 2011-05-25