Because we use only a subset of the original data for the construction of the random decision tree, we may not have enough features to form a full tree that is able to classify every feature. In such cases, a tree will not return any class for a feature that should be classified. Therefore, we will only consider trees that classify a feature to some specific class.

The feature we would like to classify is: ['Good', 'Cold', '?']. A random decision tree votes for the class to which it classifies a given feature using the same method to classify a feature as in the previous chapter on decision trees. Tree 0 votes for the class: No. Tree 1 votes for the class: No. The class with the maximum number of votes is 'No'. Therefore, the constructed random forest classifies the feature ['Good', 'Cold', '?'] into the class 'No'.