Added user guide entry for bagging classifiers
diff --git a/docs/gitbook/SUMMARY.md b/docs/gitbook/SUMMARY.md
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--- a/docs/gitbook/SUMMARY.md
+++ b/docs/gitbook/SUMMARY.md
@@ -101,6 +101,7 @@
* [Perceptron, Passive Aggressive](binaryclass/news20_pa.md)
* [CW, AROW, SCW](binaryclass/news20_scw.md)
* [General Binary Classifier](binaryclass/news20_generic.md)
+ * [Baggnig classiers](binaryclass/news20_generic_bagging.md)
* [AdaGradRDA, AdaGrad, AdaDelta](binaryclass/news20_adagrad.md)
* [Random Forest](binaryclass/news20_rf.md)
* [XGBoost](binaryclass/news20b_xgboost.md)
diff --git a/docs/gitbook/binaryclass/news20_generic_bagging.md b/docs/gitbook/binaryclass/news20_generic_bagging.md
new file mode 100644
index 0000000..ba5b949
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+++ b/docs/gitbook/binaryclass/news20_generic_bagging.md
@@ -0,0 +1,112 @@
+<!--
+ 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
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+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+-->
+
+Hivemall generally uses model averaging (i.e., model ensemble) for creating a unified prediction model.
+In this tutorial, we show how to apply bagging (i.e., prediction ensemble) for making a prediction.
+
+<!-- toc -->
+
+## Training
+
+```sql
+-- set mapred.reduce.tasks=3; -- explicitly use 3 reducers
+
+CREATE TABLE bagging_models
+as
+WITH train as (
+ select
+ train_classifier(
+ add_bias(features), label,
+ '-loss logistic -opt AdamHD -reg l1 -iters 20'
+ ) as (feature,weight)
+ from
+ news20b_train_x3
+),
+models as (
+ select
+ taskid() as modelid,
+ feature,
+ weight
+ from
+ train
+)
+select
+ modelid,
+ feature,
+ voted_avg(weight) as weight -- or simply avg(weight)
+from
+ models
+group by
+ modelid, feature;
+```
+
+## prediction
+
+```sql
+create table bagging_predict
+as
+WITH weights as (
+ select
+ t.rowid,
+ m.modelid,
+ sum(m.weight * t.value) as total_weight
+ from
+ news20b_test_exploded t
+ LEFT OUTER JOIN
+ bagging_models m ON (t.feature = m.feature)
+ group by
+ rowid, modelid
+),
+voted as (
+ select
+ rowid,
+ voted_avg(total_weight) as total_weight
+ from
+ weights
+ group by
+ rowid
+)
+select
+ rowid,
+ max(total_weight) as total_weight, -- max is dummy
+ case when sum(total_weight) > 0.0 then 1 else -1 end as label
+from
+ voted
+group by
+ rowid;
+```
+
+## evaluation
+
+```sql
+WITH submit as (
+ select
+ t.label as actual,
+ p.label as predicted
+ from
+ news20b_test t
+ JOIN bagging_predict p on (t.rowid = p.rowid)
+)
+select
+ sum(if(actual = predicted, 1, 0)) / count(1) as accuracy
+from
+ submit;
+```
+
+> 0.9641713370696557
\ No newline at end of file
