Bagging | Ignite Documentation

Virtual Ignite Summit—June 14th—Full Agenda

Edit

Bagging

Bagging stands for bootstrap aggregation. One way to reduce the variance of an estimate is to average together multiple estimates. For example, we can train M different trees on different subsets of the data (chosen randomly with replacement) and compute the ensemble:

bagging

Bagging uses bootstrap sampling to obtain the data subsets for training the base learners. For aggregating the outputs of base learners, bagging uses voting for classification and averaging for regression.

// Define the weak classifier.
DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);

// Set up the bagging process.
BaggedTrainer<Double> baggedTrainer = TrainerTransformers.makeBagged(
  trainer, // Trainer for making bagged
  10,      // Size of ensemble
  0.6,     // Subsample ratio to whole dataset
  4,       // Feature vector dimensionality
  3,       // Feature subspace dimensionality
  new OnMajorityPredictionsAggregator())
  .withEnvironmentBuilder(LearningEnvironmentBuilder
                          .defaultBuilder()
                          .withRNGSeed(1)
                         );

// Train the Bagged Model.
BaggedModel mdl = baggedTrainer.fit(
  ignite,
  dataCache,
  vectorizer
);
Tip
A commonly used class of ensemble algorithms are forests of randomized trees.

Example

The full example could be found as a part of the Titanic tutorial here.