Random Subspace Ensemble is a machine learning algorithm that combines the predictions from multiple decision trees trained on different subsets of columns in the training dataset.
Randomly varying the columns used to train each contributing member of the ensemble has the effect of introducing diversity into the ensemble and, in turn, can lift performance over using a single decision tree.
It is related to other ensembles of decision trees such as bootstrap aggregation (bagging) that creates trees using different samples of rows from the training dataset, and random forest that combines ideas from bagging and the random subspace ensemble.
Although decision trees are often used, the general random subspace method can be used with any machine learning model whose performance varies meaningfully with the choice of input features.
In this tutorial, you will discover how to develop random subspace ensembles for classification and regression.
After completing this tutorial, you will know:
Let’s get started.
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