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Ensemble modeling - from whatis.com
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Ensemble
modeling is the process of running two or more related but
different analytical models and then synthesizing the results into a single
score or spread in order to improve the accuracy of predictive analytics and
data mining applications.
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In
predictive modeling and other types of data analytics, a single model based
on one data sample can have biases, high variability or outright inaccuracies
that affect the reliability of its analytical findings. Using specific
modeling techniques can present similar drawbacks. By combining different
models or analyzing multiple samples, data scientists and other data analysts
can reduce the effects of those limitations and provide better information to
business decision makers.
One common
example of ensemble modeling is a random forest model. This approach to data
mining leverages multiple decision trees, a type of analytical model that's
designed to predict outcomes based on different variables and rules. A random
forest model blends decision trees that may analyze different sample data,
evaluate different factors or weight common variables differently. The
results of the various decision trees are then either converted into a simple
average or aggregated through further weighting.
Ensemble
modeling has grown in popularity as more organizations have deployed the
computing resources and advanced analytics software needed to run such
models. In addition, the emergence of Hadoop and other big data technologies
has led businesses to store and analyze greater volumes of data, creating
increased potential for running analytical models on different data samples. |
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