apache-ignite

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// Licensed to the Apache Software Foundation (ASF) under one or more
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// contributor license agreements.  See the NOTICE file distributed with
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// this work for additional information regarding copyright ownership.
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// The ASF licenses this file to You under the Apache License, Version 2.0
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// (the "License"); you may not use this file except in compliance with
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// the License.  You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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= Bagging
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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:
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image::images/bagging.png[]
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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.
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[source, java]
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----
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// Define the weak classifier.
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DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
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// Set up the bagging process.
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BaggedTrainer<Double> baggedTrainer = TrainerTransformers.makeBagged(
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  trainer, // Trainer for making bagged
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  10,      // Size of ensemble
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  0.6,     // Subsample ratio to whole dataset
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  4,       // Feature vector dimensionality
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  3,       // Feature subspace dimensionality
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  new OnMajorityPredictionsAggregator())
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  .withEnvironmentBuilder(LearningEnvironmentBuilder
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                          .defaultBuilder()
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                          .withRNGSeed(1)
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                         );
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// Train the Bagged Model.
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BaggedModel mdl = baggedTrainer.fit(
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  ignite,
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  dataCache,
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  vectorizer
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);
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----
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TIP: A commonly used class of ensemble algorithms are forests of randomized trees.
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== Example
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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].
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