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Machine Learning tutorial: How to create a decision tree in RapidMiner using the Titanic passenger data set



Greetings! And welcome to another wam bam, thank you ma'am, mind blowing, flex showing, machine learning tutorial here at!

This tutorial is based on a machine learning toolkit called RapidMiner by RapidI.  RapidMiner is a full featured Java based open source machine learning toolkit with support for all of the popular machine learning algorithms used in data analytics today.  The library supports supports the following machine learning algorithms (to name a few):

  • k-NN
  • Naive Bayes (kernel)
  • Decision Tree (Weight-based, Multiway)
  • Decision Stump
  • Random Tree
  • Random Forest
  • Neural Networks
  • Perception
  • Linear Regression
  • Polynomial Regression
  • Vector Linear Regression
  • Gaussian Process
  • Support Vector Machine (Linear, Evolutionary, PSO)
  • Additive Regression
  • Relative Regression
  • k-Means (kernel, fast)
  • And much much more!!
Excited yet?  I thought so!

How to create a decision tree using RapidMiner

When I first ran across screen shots of RapidMiner online, I thought to myself, "Oh boy.. I wonder how much this is going to cost...".  The UI looked so amazing.  It's like Visual Studio for Data Mining and Machine learning!  Much to my surprise, I found out that the application is open source and free!

Here is a quote from the RapidMiner site:

RapidMiner is unquestionably the world-leading open-source system for data mining. It is available as a stand-alone application for data analysis and as a data mining engine for the integration into own products. Thousands of applications of RapidMiner in more than 40 countries give their users a competitive edge.

I've been trying some machine learning "challenges" recently to sharpen my skills as a data scientist, and I decided to use RapidMiner to tackle the machine learning challenge called "Titanic: Machine Learning from Disaster" .  The data set is a CSV file that contains information on many of the passengers of the infamous Titanic voyage.  The goal of the challenge is to take one CSV file containing training data (the training data contains all attributes as well as the label Survived) and a testing data file containing only the attributes (no Survived label) and to predict the Survived label of the testing set based on the training set.

Warning: Although I'm not going to provide the complete solution to this challenge, I warn you, if you are working on this challenge, then you should probably stop reading this tutorial.  I do provide some insights into the survival data found in the training data set.  It's best to try to work the challenge out on your own.  After all, we learn by TRYING, FAILING, TRYING AGAIN, THEN SUCCEEDING.  I'd also like to say that I'm going to do my very best to go easy on the THEORY of this post..  I know that some of my readers like to get straight to the action :)  You have been warned..


Why a decision tree?

A decision tree model is a great way to visualize a data set to determine which attributes of a data set influenced a particular classification (label).  A decision tree looks like a tree with branches, flipped upside down..  Perhaps a (cheesy) image will illustrate..


After you are finished laughing at my drawing, we may proceed.......  OK

In my example, imagine that we have a data set that has data that is related to lifestyle and heart disease.  Each row has a person, their sex, age, Smoker (y/n), Diet (good/poor), and a label Risk (Less Risk/More Risk).  The data indicates that the biggest influence on Risk turns out to be the Smoker attribute.  Smoker becomes the first branch in our tree.  For Smokers, the next influencial attribute happens to be Age, however, for non smokers, the data indicates that their diet has a bigger influence on the risk.  The tree will branch into two different nodes until the classification os reached or the maximum "depth" that we establish is reached.  So as you can see, a decision tree can be a great way to visualize how a decision is derived based on the attributes in your data.

RapidMiner and data modeling

Ready to see how easy it is to create a prediction model using RapidMiner?  I thought so!

Create a new process

When you are working in RapidMiner, your project is known as a process.  So we will start by running RapidMiner and creating a new process.



The version of RapidMiner used in this tutorial is version 5.3.  Once the application is open, you will be presented with the following start screen.

 From this screen you will click on New Process

 You are presented with the main user interface for RapidMiner.  One of the most compelling aspects of Rapidminer is it's ease of use and intuitive user interface.  The basic flow of this process is as follows:

  • Import your test and training data from CSV files into your RapidMiner repository.  This can be found in the repository menu under Import CSV file
  • Once your data has been imported into your repository, the datasets can be dragged onto your process surface for you to apply operators
  • You will add your training data to the process
  • Next, you will add your testing data to the process
  • Search the operators for Decision Tree and add the operator
  • In order to use your training data to generate a prediction on your testing data using the Decision Tree model, we will add an "Apply Model" operator to the process.  This operator has an input that you will associate with the output model of your Decision Tree operator.  There is also an input that takes "unlearned" data from the output of your testing dataset.
  • You will attach the outputs of Apply Model to the results connectors on the right side of the process surface.
  • Once you have designed your model, RapidMiner will show you any problems with your process and will offer "Quick fixes" if they exists that you can double click to resolve.  
  • Once all problems have been resolved, you can run your process and you will see the results that you wired up to the results side of the process surface.
  • Here are screenshots of the entire process for your review

 Empty Process


Add the training data from the repository by dragging and dropping the dataset that you imported from your CSV file


Repeat the process and add the testing data underneath the training data

Now you can search in the operators window for Decision Tree operator.  Add it to your process.

The way that you associate the inputs and outputs of operators and data sets is by clicking on the output of one item and connecting it by clicking on the input of another item.  Here we are connecting the output of the training dataset to the input of the Decision Tree operator.


Next we will add the Apply model operator

Then we will create the appropriate connections for the model

Observe the quick fixes in the problems window at the bottom.. you can double click the quick fixes to resolve the issues.

You will be prompted to make a simple decision regarding the problem that was detected.  Once you resolve one problem, other problems may appear.  be sure to resolve all problems so that you can run your process.

Here is the process after resolving all problems.


Next, I select the decision tree operator and I adjust the following parameters:

Maximum Depth: change from 20 to 5.

check both boxes to make sure that the tree is not "pruned".

Once this has been done, you can Run your process and observe the results.  Since we connected both the model as well as the labeled result to the output connectors of the process, we are presented with a visual display of our Decision Tree (model) as well as the Test data set with the prediction applied.

(Decision Tree Model)


(The example test result set with the predictions applied)


As you can see, RapidMiner makes complex data analysis and machine learning tasks extremely easy with very little effort.

This concludes my tutorial on creating Decision Trees in RapidMiner.

Until next time,


Buddy James


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About the author

My name is Buddy James.  I'm a Microsoft Certified Solutions Developer from the Nashville, TN area.  I'm a Software Engineer, an author, a blogger (, a mentor, a thought leader, a technologist, a data scientist, and a husband.  I enjoy working with design patterns, data mining, c#, WPF, Silverlight, WinRT, XAML, ASP.NET, python, CouchDB, RavenDB, Hadoop, Android(MonoDroid), iOS (MonoTouch), and Machine Learning. I love technology and I love to develop software, collect data, analyze the data, and learn from the data.  When I'm not coding,  I'm determined to make a difference in the world by using data and machine learning techniques. (follow me at @budbjames).  

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Machine learning resources for .NET developers

Greetings friends and welcome to this article on Machine learning libraries for .NET developers.  Machine learning is a hot topic right now and for good reason.  Personally, I haven't been so excited about a technology since my computer used my 2800 baud modem to dial into a BBS over 17 years ago.  The thought that my computer could communicate with another computer was so fascinating to me.  That moment was the very moment that would forever change my life.  I learned a lot about DOS by writing batch scripts and running other programs that allowed me to visit and then run a BBS system.  It eventually lead me to QBasic.  I wanted to learn to write BBS door games and QBasic was included as a part of a standard DOS installation back then. Fast forward 17 years and I'm still in love with computers, programming, and the concept of communication between machines.  The magic never disappeared.  So when i first learned about the concept of Machine learning, I felt like that 13 year old kid again.  The idea that a machine can learn to do things that it has not been programmed to do is now a passion of mine.  The concepts of Machine learning have an extreme learning curve, however, I believe that we as humans can do anything that we put our mind to.  So I began looking around for tutorials on machine learning.  I found many great tutorials and books, however, most of them involved using python.  I have nothing against python.  As a matter of fact, I find it ironic that I started with BASIC and now in this moment of "rebirth" I'm beginning to use python which looks a lot like BASIC in many ways.  The fact of the matter remains, I'm a .NET developer.  I've spent the last 9 years in the .NET framework and I love the technology.  C# is an awesome programming language and it's hard to imagine life without Visual Studio.  What can I say, the IDE has spoiled me. While I scoured the internet looking for tutorials related to Machine learning resources for .NET developers, I wished that there was a one resource that would assist me in my search for resources to help me achieve my goal. Well that's what this article is all about.  In this article, I will introduce you to some .NET libraries that will assist you in your quest to learn about Machine learning. NND Neural Network Designer by Bragisoft The Neural Network Designer project (NND) is a DBMS management system for neural networks that was created by Jan Bogaerts.  The designer application is developed using WPF, and is a user interface which allows you to design your neural network, query the network, create and configure chat bots that are capable of asking questions and learning from your feed back.  The chat bots can even scrape the internet for information to return in their output as well as to use for learning.  The project includes a custom language syntax called NNL (neural network language) that you can use in configuring your machine learning project.  The source code is designed so that the libraries can be used in your own custom applications so you don't have to start from scratch with such a complex set of technologies.  The project is actually an open source project in which I am a part of.  Some of the possibilities offered by this awesome project include predictions, image and pattern recognition, value inspection, memory profiling and much more.  Stop by the Bragisoft NND website and download the application to give it a try.   Screen shots of the neural network designer by Bragisoft A DBMS for neural networks   Mind map rand forrest The chat bot designer and other tools Here is a description from the Accord.NET project website  Accord.NET is a framework for scientific computing in .NET. The framework builds upon AForge.NET, an also popular framework for image processing, supplying new tools and libraries. Those libraries encompass a wide range of scientific computing applications, such as statistical data processing, machine learning, pattern recognition, including but not limited to, computer vision and computer audition. The framework offers a large number of probability distributions, hypothesis tests, kernel functions and support for most popular performance measurements techniques.  The most impressive parts of this library has got to be the documentation and sample applications that are distributed with the project.  This makes the library easy to get started using.  I also like the ability to perform operations like Audio processing (beat detection and more), Video processing (easy integration with your web cam, vision capabilities and object recognition).  This is an excellent place to start with approaching Machine learning with the .NET framework.  Here are a two videos that should whet your appetite. Hand writing recognition with Accord.NET   Here is an example of head tracking with Accord.NET (super cool)   AIMLBot Program# AILM Chat bot library AIMLBot (Program#) is a small, fast, standards-compliant yet easily customizable implementation of an AIML (Artificial Intelligence Markup Language) based chatter bot in C#. AIMLBot has been tested on both Microsoft's runtime environment and Mono. Put simply, it will allow you to chat (by entering text) with your computer using natural language.  The project is located here. Math.NET Machine learning algorithms are extremely math heavy.  Math.NET is a library  that can assist with the math that is required to solve machine learning related problems. Math.NET Numerics aims to provide methods and algorithms for numerical computations in science, engineering and every day use. Covered topics include special functions, linear algebra, probability models, random numbers, interpolation, integral transforms and more. DotNumerics DotNumerics is a website dedicated to numerical computing for .NET. DotNumerics includes a Numerical Library for .NET. The library is written in pure C# and has more than 100,000 lines of code with the most advanced algorithms for Linear Algebra, Differential Equations and Optimization problems. The Linear Algebra library includes CSLapack, CSBlas and CSEispack, these libraries are the translation from Fortran to C# of LAPACK, BLAS and EISPACK, respectively. You can find the library here.  ALGLIB ALGLIB is a cross-platform numerical analysis and data processing library. It supports several programming languages (C++, C#, Pascal, VBA) and several operating systems (Windows, Linux, Solaris). ALGLIB features include: Accessing ‘R’ from C#–Lessons learned Here are instructions to use the R statistical framework from within c# ILNumerics You can check out the library at A nice site about the basics of machine learning in c# by Seth Juarez . NuML.NET is a machine learning library for .NET developers written by Seth Juarez.  I've recently tried this library and I'm impressed!  Seth has stated publicly that his intention behind the library is to abstract the scary math away from machine learning to provide tools that are more approachable by software developers and boy did he deliver!  I've been working with this library for a little more than an hour and I've written a prediction app in c#.  You can find his library source on github. Encog Machine Learning Framework Here is what the official Heaton Research website has to say about Encog: Encog is an advanced machine learning framework that supports a variety of advanced algorithms, as well as support classes to normalize and process data. Machine learning algorithms such as Support Vector Machines, Artificial Neural Networks, Genetic Programming, Bayesian Networks, Hidden Markov Models and Genetic Algorithms are supported. Most Encog training algoritms are multi-threaded and scale well to multicore hardware. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train machine learning algorithms. Encog has been in active development since 2008. Encog is available for Java, .Net and C/C++. Jeff Heaton knows a great deal about machine learning algorithms and he's created a wonderful library called Encog.  I was able to write a neural network application that solved the classic XOR problem in 20 minutes after installing the library.  What really amazes me is that he has an Encog Library for JavaScript which includes live samples on his website of Javascript + encog solving problems like the Traveling Salesman Problem and Conway's game of life, all in a browser!  This library can even use your GPU for the heavy lifting if that's your choice.  I would highly recommend that you at least check out his site and download the library to look at the examples.  You can find the Encog library here.    Conclusion This concludes my article on Machine learning resources for the .NET developer.  If you have any suggestions regarding a project that you know of or you are working on related to Machine learning in .NET, please don't hesitate to leave a comment and I will update the article to mention the project.  This article has shown that we as .NET developers have many resources available to us to use to implement Machine learning based solutions.  I appreciate your time in reading this article and I hope you found it useful.  Please subscribe to my RSS feed.  Until next time.. Buddy James

Machine Learning: 5 examples of what it is and why you should care

Machine learning examples to make you think Hello folks, and welcome to another awe inspiring article from .  This article is one that I'm very excited to present.  I'm sure you've guessed by now that the topic of this post is Machine learning.   If you don't know what machine learning is or don't care, I ask you to take a look at the fascinating examples that I've presented in this article.  You just may get inspired.   Background I'm a .NET developer and I have experience working in a myriad of different business domains.  My love affair with machine learning was brought about while I was working for an e-commerce website.  My boss had asked me to look into a new API that Google had released in beta at the time that would allow you to provide data about your customers and it would suggest products based on their shopping data.  That API is called Google prediction and you can read about the Google prediction API here.  It's been a couple of years since I was introduced to this technology and since that time I've contemplated how machine learning algorithms work and what possibilities they could unlock with the right amount of data and creativity. Since then I've done a lot of reading, and planning on ways that I can collect data to use in my journey to learn as much as I can about this new frontier that we as software developers are facing.  I was fortunate enough become the first accepted team member of the open source project called NND, or Neural Network Designer by Bragisoft.  Check back soon for an article dedicated to this wonderful open source project on machine learning and neural networks. Proceed with caution Let it be known, however, that machine learning algorithms are not for the faint of heart.  This is a very complex array of concepts and I don't plan to  try to explain them in this article.  What I will do is give a brief, simple introduction to a few of the prevalent topics that one would need to research in order to implement machine learning algorithms.  The main purpose of this article is to provide some wonderful youtube videos that provide insight into the possibilities of machine learning and it's practical applications.  Some of the videos just may blow your mind! So without further ado, let's bring forth the videos! A glimpse at the future to whet your appetite  This first video is full of commentary and stunning examples of robots that lack brains but are capable of learning by way of a design that mimics a central nervous system.  Although the video depicts the future as something to be worried about, it's still a great, non technical introduction to whet your appetite and make way for the other more specific videos that are focused on simulations and applications of machine learning. Watch a simulation of robots that learn to drive In this video, we see a software demonstration of animated robots that start with no knowledge of a driving course.  Watch what happens as the simulation is processed repeatedly, allowing the robots to learn the course with each generation.  Buckle up! Hey Darwin, what do you think of this? This next video is progressively more complex, however, the simulation clearly shows how a collection of bots act as they evolve.  The bots  can attack each other with the intent of killing and eating each other to sustain life.  They also reproduce to make baby bots.  Take a look at this awesome example of machine learning.. Virtual Darwinism take two This next video is another representation of virtual evolution by machine learning techniques.  Watch block like organisms learn to fight each other over a virtual cube that represents a block of food.   Machine learning for pattern matching and recognition This video shows an application that will render CAPTCHA verification methods obsolete.  I have a decent amount of experience working with OCR engines however, this is the most accurate recognition I've ever seen. Show and tell This concludes my article on machine learning examples.  I hope you enjoyed the article and that it has peaked your interest in machine learning.  Do you have a favorite video or application that demonstrates machine learning, neural networks, prediction algorithms, pattern matching or some other related technology?  If so, we'd love to hear about it so please leave a comment with a link. Thanks for reading, Buddy James kick it on

About the author

My name is Buddy James.  I'm a Microsoft Certified Solutions Developer from the Nashville, TN area.  I'm a Software Engineer, an author, a blogger (, a mentor, a thought leader, a technologist, a data scientist, and a husband.  I enjoy working with design patterns, data mining, c#, WPF, Silverlight, WinRT, XAML, ASP.NET, python, CouchDB, RavenDB, Hadoop, Android(MonoDroid), iOS (MonoTouch), and Machine Learning. I love technology and I love to develop software, collect data, analyze the data, and learn from the data.  When I'm not coding,  I'm determined to make a difference in the world by using data and machine learning techniques. (follow me at @budbjames).  

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