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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// 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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= Introduction
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In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.  Typically, ML ensemble consists of only a concrete finite set of alternative models.
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Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking).
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The most popular ensemble models are supported in Apache Ignite ML:
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* Stacking
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* Boosting via GradientBoosting
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* Bagging (Bootstrap aggregating) and RandomForest as a special case
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