Archives for Boosting


the idea behind stack ensemble method is to handle a machine learning problem using different types of models that are capable of learning to an extent, not the whole space of the problem. Using these models we can make intermediate predictions and then add a new model that can learn using the intermediate predictions.


In machine learning, ensemble approaches combine many weak learners to achieve better prediction performance than each of the constituent learning algorithms alone.
Ensemble Learning is the process of gathering more than one machine learning model in a mathematical way to obtain better performance.
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In Machine Learning context, there are typically two kinds of learners or algorithms, ones that learn well the correlations and gives out strong predictions and the ones which are lazy and gives out average predictions that are slightly better than random selection or guessing. The algorithms that fall into the former category are referred to…
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