May improve the learning by adding new features to the training data based on the decision trees from a previously learned model.
xgb.create.features(model, data, ...)
decision tree boosting model learned on the original data
original data (usually provided as a
currently not used
This is the function inspired from the paragraph 3.1 of the paper:
Practical Lessons from Predicting Clicks on Ads at Facebook
(Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yan, xin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, Joaquin Quinonero Candela)
International Workshop on Data Mining for Online Advertising (ADKDD) - August 24, 2014
Extract explaining the method:
"We found that boosted decision trees are a powerful and very convenient way to implement non-linear and tuple transformations of the kind we just described. We treat each individual tree as a categorical feature that takes as value the index of the leaf an instance ends up falling in. We use 1-of-K coding of this type of features.
For example, consider the boosted tree model in Figure 1 with 2 subtrees,
where the first subtree has 3 leafs and the second 2 leafs. If an
instance ends up in leaf 2 in the first subtree and leaf 1 in
second subtree, the overall input to the linear classifier will
be the binary vector
[0, 1, 0, 1, 0], where the first 3 entries
correspond to the leaves of the first subtree and last 2 to
those of the second subtree.
We can understand boosted decision tree based transformation as a supervised feature encoding that converts a real-valued vector into a compact binary-valued vector. A traversal from root node to a leaf node represents a rule on certain features."
dgCMatrix matrix including both the original data and the new features.
data(agaricus.train, package='xgboost') data(agaricus.test, package='xgboost') dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label)) dtest <- with(agaricus.test, xgb.DMatrix(data, label = label)) param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic') nrounds = 4 bst = xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2) # Model accuracy without new features accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) / length(agaricus.test$label) # Convert previous features to one hot encoding new.features.train <- xgb.create.features(model = bst, agaricus.train$data) new.features.test <- xgb.create.features(model = bst, agaricus.test$data) # learning with new features new.dtrain <- xgb.DMatrix(data = new.features.train, label = agaricus.train$label) new.dtest <- xgb.DMatrix(data = new.features.test, label = agaricus.test$label) watchlist <- list(train = new.dtrain) bst <- xgb.train(params = param, data = new.dtrain, nrounds = nrounds, nthread = 2) # Model accuracy with new features accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) / length(agaricus.test$label) # Here the accuracy was already good and is now perfect. cat(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now", accuracy.after, "!\n"))
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