diff --git a/gnu/packages/machine-learning.scm b/gnu/packages/machine-learning.scm
index b35e9b72c6..cfeb1daf63 100644
--- a/gnu/packages/machine-learning.scm
+++ b/gnu/packages/machine-learning.scm
@@ -23,7 +23,12 @@
   #:use-module (guix download)
   #:use-module (guix build-system gnu)
   #:use-module (gnu packages)
-  #:use-module (gnu packages python))
+  #:use-module (gnu packages boost)
+  #:use-module (gnu packages compression)
+  #:use-module (gnu packages gcc)
+  #:use-module (gnu packages maths)
+  #:use-module (gnu packages python)
+  #:use-module (gnu packages xml))
 
 (define-public libsvm
   (package
@@ -96,3 +101,46 @@ classification.")
     (inputs
      `(("python" ,python)))
     (synopsis "Python bindings of libSVM")))
+
+(define-public randomjungle
+  (package
+    (name "randomjungle")
+    (version "2.1.0")
+    (source
+     (origin
+       (method url-fetch)
+       (uri (string-append
+             "http://www.imbs-luebeck.de/imbs/sites/default/files/u59/"
+             "randomjungle-" version ".tar_.gz"))
+       (sha256
+        (base32
+         "12c8rf30cla71swx2mf4ww9mfd8jbdw5lnxd7dxhyw1ygrvg6y4w"))))
+    (build-system gnu-build-system)
+    (arguments
+     `(#:configure-flags
+       (list (string-append "--with-boost="
+                            (assoc-ref %build-inputs "boost")))
+       #:phases
+       (modify-phases %standard-phases
+         (add-before
+          'configure 'set-CXXFLAGS
+          (lambda _
+            (setenv "CXXFLAGS" "-fpermissive ")
+            #t)))))
+    (inputs
+     `(("boost" ,boost)
+       ("gsl" ,gsl)
+       ("libxml2" ,libxml2)
+       ("zlib" ,zlib)))
+    (native-inputs
+     `(("gfortran" ,gfortran-4.8)))
+    (home-page "http://www.imbs-luebeck.de/imbs/de/node/227/")
+    (synopsis "Implementation of the Random Forests machine learning method")
+    (description
+     "Random Jungle is an implementation of Random Forests.  It is supposed to
+analyse high dimensional data.  In genetics, it can be used for analysing big
+Genome Wide Association (GWA) data.  Random Forests is a powerful machine
+learning method.  Most interesting features are variable selection, missing
+value imputation, classifier creation, generalization error estimation and
+sample proximities between pairs of cases.")
+    (license license:gpl3+)))