How to Develop a Random Subspace Ensemble With Python

How to Develop a Random Subspace Ensemble With Python

How to Develop a Random Subspace Ensemble With Python. In this tutorial, you will discover how to develop random subspace ensembles for classification and regression.

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:

  • Random subspace ensembles are created from decision trees fit on different samples of features (columns) in the training dataset.
  • How to use the random subspace ensemble for classification and regression with scikit-learn.
  • How to explore the effect of random subspace model hyperparameters on model performance.

Let’s get started.

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