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"<p><a href=\"/definition/30330/ensemble-learning\">Ensemble learning</a> has various benefits for <a href=\"/definition/8181/machine-learning\">machine learning</a> projects. Many of these are related to using a large number of relatively simple nodes to aggregate some inputs and output results.</p><p>For example, ensemble learning can help <a href=\"/definition/677/project-manager-pm\">project managers</a> to deal with both <a href=\"/definition/33036/machine-bias\">bias</a> and variance &mdash; variance representing scattered results that are difficult to converge, and bias representing miscalibration or error in targeting necessary results.</p><p>There&rsquo;s long and involved mathematical analysis of how each of these solutions works, along with various practices like <a href=\"/definition/33212/boosting\">boosting</a> and <a href=\"/definition/33202/bagging\">bagging</a>, but for those who aren&rsquo;t personally involved in <a href=\"https://www.altaml.com/#utm_source=Techopedia&utm_medium=Sponsored&utm_campaign=Techopedia\" rel=\"_blank\">machine learning</a>, it may be enough to understand that ensemble learning basically brings a decentralized, consensus-based approach to machine learning that helps to refine results and ensure precision. Think of ensemble learning as the essential <a href=\"/definition/27816/crowdsourcing\">&ldquo;crowdsourcing&rdquo;</a> of points of input in order to come up with a big picture analysis. In a sense, this is what machine learning is all about, and <a href=\"/definition/33213/adaboost\">AdaBoost</a> or related systems do this through an ensemble learning approach. Another way to boil this concept down to its basics is to think about the old slogan: &ldquo;two heads are better than one&rdquo; and think about how decentralizing sourcing or control helps to come up with more precise results.</p><p>One example of ensemble learning is a <a href=\"/definition/32935/random-forest\">random forest</a> approach. In a random forest, a group of <a href=\"/definition/28634/decision-tree\">decision trees</a> has some overlapping material, and some unique results that are blended together to achieve a goal with mathematical and methodical outcome. This is an example of how ensemble learning works practically to support <a href=\"https://www.altaml.com/#utm_source=Techopedia&utm_medium=Sponsored&utm_campaign=Techopedia\" rel=\"_blank\">better machine learning</a> in <a href=\"/definition/5967/artificial-neural-network-ann\">neural networks</a> and other systems. In a basic sense, the data &ldquo;merges&rdquo; and is stronger for its decentralized origins.</p>"
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