diff --git a/data b/data
index 7b23694342c..b9b2d9058c2 160000
--- a/data
+++ b/data
@@ -1 +1 @@
-Subproject commit 7b23694342c8954f470da3a861511ed4465a6ced
+Subproject commit b9b2d9058c22dae86a4181db7271dfa02485af7d
diff --git a/doc/cookbook/source/examples/multiclass_classifier/shareboost.rst b/doc/cookbook/source/examples/multiclass_classifier/shareboost.rst
new file mode 100644
index 00000000000..001d42857a1
--- /dev/null
+++ b/doc/cookbook/source/examples/multiclass_classifier/shareboost.rst
@@ -0,0 +1,33 @@
+==========
+ShareBoost
+==========
+
+ShareBoost algorithm learns a multiclass predictor from a subset of shared features of the samples with forward greedy selection approach.
+
+See :cite:`shalev2011shareboost` for a detailed introduction.
+
+-------
+Example
+-------
+Imagine we have files with training and test data. We create CDenseFeatures (here 64 bit floats aka RealFeatures) and :sgclass:`CMulticlassLabels` as
+
+.. sgexample:: shareboost.sg:create_features
+
+We create an instance of the :sgclass:`CShareBoost` classifier by setting the number of features expected to be used for learning.
+
+.. sgexample:: shareboost.sg:create_instance
+
+Then we train and apply it to test data, which gives :sgclass:`CMulticlassLabels`.
+
+.. sgexample:: shareboost.sg:train_and_apply
+
+We can evaluate test performance via e.g. :sgclass:`CMulticlassAccuracy`.
+
+.. sgexample:: shareboost.sg:evaluate_accuracy
+
+----------
+References
+----------
+
+.. bibliography:: ../../references.bib
+    :filter: docname in docnames
diff --git a/doc/cookbook/source/references.bib b/doc/cookbook/source/references.bib
index 91f242f88f3..3322b8cf452 100644
--- a/doc/cookbook/source/references.bib
+++ b/doc/cookbook/source/references.bib
@@ -79,3 +79,10 @@ @inproceedings{gao2011discriminative
   year={2011},
   organization={IEEE}
 }
+@inproceedings{shalev2011shareboost,
+  title={Shareboost: Efficient multiclass learning with feature sharing},
+  author={Shalev-Shwartz, Shai and Wexler, Yonatan and Shashua, Amnon},
+  booktitle={Advances in Neural Information Processing Systems},
+  pages={1179--1187},
+  year={2011}
+}
diff --git a/examples/meta/generator/targets/cpp.json b/examples/meta/generator/targets/cpp.json
index fa4b8583944..03496a0cb94 100644
--- a/examples/meta/generator/targets/cpp.json
+++ b/examples/meta/generator/targets/cpp.json
@@ -15,6 +15,7 @@
     "Assign": "$lhs = $expr",
     "Type": {
         "RealFeatures": "DenseFeatures<float64_t>",
+        "RealSubsetFeatures": "DenseSubsetFeatures<float64_t>",
         "StringCharFeatures": "CStringFeatures<char>",
         "Default": "$type",
         "bool": "bool",
diff --git a/examples/meta/src/multiclass_classifier/shareboost.sg b/examples/meta/src/multiclass_classifier/shareboost.sg
new file mode 100644
index 00000000000..94bf52614db
--- /dev/null
+++ b/examples/meta/src/multiclass_classifier/shareboost.sg
@@ -0,0 +1,29 @@
+CSVFile f_feats_train("../../data/classifier_4class_2d_linear_features_train.dat")
+CSVFile f_feats_test("../../data/classifier_4class_2d_linear_features_test.dat")
+CSVFile f_labels_train("../../data/classifier_4class_2d_linear_labels_train.dat")
+CSVFile f_labels_test("../../data/classifier_4class_2d_linear_labels_test.dat")
+
+#![create_features]
+RealFeatures features_train(f_feats_train)
+RealFeatures features_test(f_feats_test)
+MulticlassLabels labels_train(f_labels_train)
+MulticlassLabels labels_test(f_labels_test)
+#![create_features]
+
+#![create_instance]
+ShareBoost shareboost(features_train, labels_train, 2)
+#![create_instance]
+
+#![train_and_apply]
+shareboost.train()
+RealSubsetFeatures features_test_sub(features_test, shareboost.get_activeset())
+MulticlassLabels labels_predict = shareboost.apply_multiclass(features_test_sub)
+#![train_and_apply]
+
+#![evaluate_accuracy]
+MulticlassAccuracy eval()
+real accuracy = eval.evaluate(labels_predict, labels_test)
+#![evaluate_accuracy]
+
+# additional integration testing variables
+RealVector output = labels_predict.get_labels()
diff --git a/examples/undocumented/python_modular/classifier_multiclass_shareboost.py b/examples/undocumented/python_modular/classifier_multiclass_shareboost.py
deleted file mode 100644
index b2c96c0080e..00000000000
--- a/examples/undocumented/python_modular/classifier_multiclass_shareboost.py
+++ /dev/null
@@ -1,39 +0,0 @@
-#!/usr/bin/env python
-from tools.multiclass_shared import prepare_data
-
-[traindat, label_traindat, testdat, label_testdat] = prepare_data(False)
-
-parameter_list = [[traindat,testdat,label_traindat,label_testdat,2.1,1,1e-5],[traindat,testdat,label_traindat,label_testdat,2.2,1,1e-5]]
-
-def classifier_multiclass_shareboost (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,label_test_multiclass=label_testdat,lawidth=2.1,C=1,epsilon=1e-5):
-    from modshogun import RealFeatures, RealSubsetFeatures, MulticlassLabels
-    from modshogun import ShareBoost
-
-    #print('Working on a problem of %d features and %d samples' % fm_train_real.shape)
-
-    feats_train = RealFeatures(fm_train_real)
-
-    labels = MulticlassLabels(label_train_multiclass)
-
-    shareboost = ShareBoost(feats_train, labels, min(fm_train_real.shape[0]-1, 30))
-    shareboost.train();
-    #print(shareboost.get_activeset())
-
-    feats_test  = RealSubsetFeatures(RealFeatures(fm_test_real), shareboost.get_activeset())
-    label_pred = shareboost.apply(feats_test)
-
-    out = label_pred.get_labels()
-
-    if label_test_multiclass is not None:
-        from modshogun import MulticlassAccuracy
-        labels_test = MulticlassLabels(label_test_multiclass)
-        evaluator = MulticlassAccuracy()
-        acc = evaluator.evaluate(label_pred, labels_test)
-        #print('Accuracy = %.4f' % acc)
-
-    return out
-
-if __name__=='__main__':
-    print('MulticlassMachine')
-    classifier_multiclass_shareboost(*parameter_list[0])
-