Review Random Forests

Before we move on, let's briefly review what we discussed in the previous lesson. Select the statements among the following that are true.

Random forests use row bagging to generate a different dataset on which to grow each tree.

The out-of-bag estimate is an estimate of the prediction error of the model.

According to the random forest algorithm, at the start of the algorithm, m of the original M data columns are selected. These are then used to grow the forest of trees.

The number of trees in the forest can be set with the parameter max_features .

The out-of-bag estimate is available to any machine learning method utilizing random column selection.

SOLUTION:

Random forests use row bagging to generate a different dataset on which to grow each tree.
The out-of-bag estimate is an estimate of the prediction error of the model.

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