docs/_docs/machine-learning/ensemble-methods/bagging.adoc

// Licensed to the Apache Software Foundation (ASF) under one or more
// contributor license agreements.  See the NOTICE file distributed with
// this work for additional information regarding copyright ownership.
// The ASF licenses this file to You under the Apache License, Version 2.0
// (the "License"); you may not use this file except in compliance with
// the License.  You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
= 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:

image::images/bagging.png[]

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.


[source, java]
----
// 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 https://github.com/apache/ignite/blob/master/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_10_Bagging.java[here].