The challenge is to blend together models from different analytics platforms - i.e. Python , R, and KNIME - to create an ensemble model. Data is the “airline data set” (http://stat-computing.org/dataexpo/2009/the-data.html) enriched with additional external data , such as cities, daily weather (https://www.ncdc.noaa.gov/cdo-web/datasets/), US holidays, geo-coordinates, airplane maintenance. DepDealys is used as the target variable. R SVM, Python Logisitc Regression, and KNIME Decision Tree. Will they blend in a single ensemble model? ... and yes! They blend.
External resources
Used extensions & nodes
Created with KNIME Analytics Platform version 4.1.2
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KNIME Core
KNIME AG, Zurich, Switzerland
Version 4.1.2
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KNIME Ensemble Learning Wrappers
KNIME AG, Zurich, Switzerland
Version 4.1.0
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KNIME Interactive R Statistics Integration
KNIME AG, Zurich, Switzerland
Version 4.1.1
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KNIME JavaScript Views
KNIME AG, Zurich, Switzerland
Version 4.1.2
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KNIME Math Expression (JEP)
KNIME AG, Zurich, Switzerland
Version 4.1.0
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KNIME Python Integration
KNIME AG, Zurich, Switzerland
Version 4.1.1
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