Neural networks principal component analysis
As a model building environment, I use KNIME to generate a neural network model for predicting customer churn. Once data pre-processing and model are represented in PMML, I go on to deploy it in the Amazon Cloud using the ADAPA Scoring Engine and on top of Hadoop using the Universal PMML Plug-in (UPPI) for Datameer. So, the very same model is readily available for execution in two very distinct Big Data platforms: cloud and Hadoop.
The easy of model deployment and interoperability between platforms is the power of PMML, the de facto standard for predictive analytics and data mining models.
- Download the KNIME workflow used to generate a sample neural network for predicting churn
- Download the PMML file created during the demo
Source: Zementis Predictive Analytics
You might also like:
15 Things You Shouldn't Be Paying For2010-08-19 09:27:17 by YouUngratefulTurds
Basic Computer Software -- Thinking of purchasing a new computer? Think twice before you fork over the funds for a bunch of extra software. There are some great alternatives to the name brand software programs. The most notable is OpenOffice, the open-source alternative to those other guys. It's completely free and files can be exported in compatible formats.
Your Credit Report -- You don't have to pay for your credit report. You could sign up for one of the free credit monitoring services online to get a quick look at your credit report. You just have to remember to cancel the service before the end of the free trial. Or you could do one better and visit the only truly free place to see all three of your credit reports for free once a year.
Cell Phone -- The service plan...
Roger Highfield: 'Raise your IQ instantly -- by no longer believing in it' — Wired.co.uk
The answer, as revealed in Neuron, is an emphatic no. You can explain the observed ... Each of the three different factors identified by the principal-components analysis did indeed correspond to a different brain network. We can now say, with ...
Artificial Neuronal Networks: Application to Ecology and Evolution (Environmental Science and Engineering / Environmental Science)