Would anyone be interested in an implementation of a ensemble decision-tree
classifier that can be trained incrementally, or know why one is not
already included in sklearn?
I have a dataset that can be segmented very cleanly by a decision tree, but
it's too large to fit into memory, so I couldn't directly apply the
DecisionTreeClassifier or RandomForestClassifier, since they don't
implement partial_fit().
So I wrote a simple implementation that maintains a forest of
DecisionTreeClassifier instances, training a new instance every time
partial_fit() is called, keeping the N best according to the value returned
by score(). The methods predict() and predict_proba() work similarly to
those in the other ensemble tree classifiers.
In my initial tests, performance and accuracy look good, but obviously it
will likely never be more accurate than a single DecisionTreeClassifier
when all your data can fit into memory. Does anyone know of any other
potential caveats to this approach?
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