Help your business stand out with decision trees

It is in your moments of decision that your destiny is shaped.” – Tony Robbins

Part 1 – Why you should include decision trees in your business solution

Decision trees are a graphical representation of a decision path. They illustrate potential solutions and selection criteria when making decisions.

It is a tool used in the majority of areas that involve decision making. As examples of questions that could be answered by a decision tree, we can think of: what treatment should a doctor choose for his patient? Which customer should be solicited by an insurance broker? What kind of product launch should a corporate decision maker prioritize?

As a human being, one of the biggest limitations to decision making is that it is difficult to choose beyond known alternatives. Decision trees help to formalize the brainstorming process and identify a multitude of possible solutions based on a large amount of data already collected. The decision that emerges naturally is not always the best; relying on verifiable data and facts is sometimes more profitable than following one’s instinct.

Why turn to a decision tree?

Why would you benefit from learning more about decision trees? To optimize your time and maximize your performance! When a virtual assistant uses a decision tree, it is possible to automate a portion of the thinking when making a decision. In today’s digital age, this type of solution is becoming increasingly popular and allows managers to focus on higher value tasks and reduce opportunities for errors. Choosing a software with a decision tree is thus a differentiator for a company that allows it to stand out from the competition, helping, for example, managers optimize their operations or increase their sales.

Let’s keep going!

Now that you are convinced of the need to learn about decision trees, here’s a summary of what you need to know:

A decision tree is able to be built by itself when provided with a training set. In the field of machine learning, therefore, the decision tree belongs to the category of supervised learning. Each entry contained in the training set includes a list of features and also provides the response value that is to be predicted. It is this combination of features / response that will be used to build the tree. Concretely :

Features                                                                                           Response
Name                Status       Age      Nb of kids      Income      Accepted our new life insurance
M. Tremblay     Married     58         5                       55 000        Yes

In this way, we can maximize the rate of positive responses by concentrating on those clients who are most likely to accept one or other option, thereby maximizing their time and increasing their income. Since a tree is as accurate as the data it relies upon, it is crucial that your set be as accurate as possible.

Types of decision trees

A decision tree can be deployed in several forms according to the criteria that will be assigned to it. In practice, two main types of trees stand out.

  • Classification : The tree is used to predict the class to which the data belongs. The response variable is qualitative. e.g : Yes / No, Buy / Sell / Keep
  • Regression : The tree is used to predict a certain value that the data will have. The response variable is quantitative. e.g : 1, 10, 50 …

How to interpret the decision tree?

A decision tree can simply be considered as presenting the possible paths of a decision. Each possible path of the tree, from the initial node to the leaves, can be considered as a decision rule.

Decision tree

Here are the different parts of the tree:

  • Nodes (rectangle) : The condition that a decision faces. These are the independent variables, and are the features listed above.
  • Branches (arrow) : The decisions taken at each node of the tree.
  • Leaves (ellipse) : The response variable that we are trying to predict. This is the final decision provided by the tree.

In the example above, considering a married customer, 58 years old and who has more than two children, it would be justified to contact him to offer him a new life insurance.

Advantages and disadvantages

Advantages

    • Simplicity : Very visual and easy to interpret. Provides more visibility and more intuitive than a neural network, for example.
    • Realism : Imitate the way humans make decisions. Give an accurate algorithm that anyone can follow.
    • Quick preparation : Very little preparation and cleaning of the data bank to be performed (no standardization required, processing of missing data or creation of dummy variables). Moreover, no hypothesis is necessary to create the tree.
    • Versatility: The model takes into account both quantitative and qualitative variables.

Disadvantages

    • Instability :  A slight change in the data can cause a change in the tree’s training and a repercussion of this change on the prediction. Having the most recent tree ensures that it represents all the data.
    • Statism : A decision tree is not autonomous if it is left to itself (mainly because of its instability). It needs to be maintained regularly, especially in changing areas. However, some tools allow the automation of the process and thus, the facilitation and acceleration of the construction of a tree.
    • Accuracy : The method is not as accurate in terms of predictive efficiency as other methods of learning. It is easy to increase the efficiency of the prediction thanks to a random forest of decision trees.
    • Over-learning : The tree is not immune to complexity and tends to be over-trained, which can cause the tree to not generalize beyond the training data. The best way to overcome over-learning is to prune the tree.

Decision tree in business

A decision tree can be added to virtually any business solution. Whether in your CRM, your transport and logistics software or in your financial system, a decision tree will find its place and will certainly improve and standardize your decision-making process. Thereby, your company will stand out from others by its performance and efficiency.

To get the maximum benefits, it is essential that this decision tree is alive and well built. Be assured that Arcbees is able to give you the best advice and support your business needs by allowing you to exceed your own goals.

Feel free to contact us directly by email or leaving a comment below. It is with great pleasure that we will discuss it with you. Our next article will guide you through each of the steps to move from a set of data to a functional tree. Don’t forget to follow Arcbees’ blog not to miss anything!

What Real-world Business Problems Can AI Solve?

In his introduction to “Could AI Solve the World’s Biggest Problems?”  published in the MIT Technology Review, Will Knight alludes to the “wealth of promising opportunities for AI applications” and reiterates the importance of using AI to make the world a better place. Fortunately enough, large organization executives, like Eric Schmidt, the chairman of Alphabet, believe AI could, in fact, help us overcome global socio-economic challenges. But what about real-world business problems?

Can AI help us tackle the smaller day-to-day issues we experience in our, sometimes, siloed departments?

Continue reading “What Real-world Business Problems Can AI Solve?”

Building Ethically Responsible AI-powered Environments

Perhaps you’ve encountered before the entreaty for the “democratization of AI.” Generally speaking, democracy has a good reputation, but what does this mean in the context of technology and artificial intelligence, and what are the dangers of the alternative? Consider the words of Andrew Ng, Chief Scientist at Baidu research, who has called AI “the new electricity,” and you’ll begin to get a sense of what we mean. At the moment, AI (and machine learning and deep learning) tools are mostly in the hands of researchers, industry, large enterprises, colleges, and labs. AI presents distinct advantages to businesses who have access to incorporating it into their systems or products, improving everything from logistical efficiency to positive user experience and everything in between; by definition, those who can’t access or incorporate AI/ML are at a disadvantage. The democratization of AI is a term that, in a nutshell, means leveling the playing field by making the advantages of AI available to all, and it’s an important aspect of the larger project of the ethical development of technology.

Continue reading “Building Ethically Responsible AI-powered Environments”

How Influential Businesses like Google Are Using Artificial Intelligence and What We Can Learn from Them

AI is a hot buzzword right now and businesses of all shapes and sizes are trying to find a way to leverage its power for their own particular needs. Exciting, futuristic, and vaguely science-fictiony, AI is currently seen as a must-have tool for turbo-charging one’s business and improving the quality of customer experience. It’s been a game changer for enterprise companies like Google, Microsoft, Amazon, and IBM who have the computing power and resources to access field experts. Small companies often aren’t in the same ball game, yet there remains the need to get in the action if they are going to keep up with the times. Fortunately, cloud computing and open-source machine learning APIs are bridging the technical gap for smaller companies, making it possible for them to take advantage of the endless possibilities offered by AI. More than ever, this is the time for small businesses to jump in and start experimenting. The real challenge, however, is knowing how to utilize these new resources well. Here again, small businesses can look to larger enterprise companies, such as Google, to gain insight and build on their ideas. Here’s what we can learn.

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Arcbees Acquires Chalifour Digital Solutions

NEWS

Québec, October 2, 2017– Arcbees, a leading technology company in artificial intelligence, announces the strategic acquisition of Chalifour Digital Solutions. This transaction will broaden the company’s areas of expertise, focus joint development efforts and strengthen its market positioning in intelligent business solutions.

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3 AI Fails and Why They Happened

In little over a decade, AI has made leaps and bounds. Every day, new headlines showcase the most recent advancement in AI. In fact, advancements are accelerating:

  • 2004 DARPA sponsors a driverless car grand challenge. Technology developed by the participants eventually allows Google to develop a driverless automobile and modify existing transportation laws;
  • 2005 Honda’s ASIMO humanoid robot is able to walk as fast as a human, delivering trays to customers in a restaurant setting. The same technology is now used in military robots;
  • 2007 Computers learned to play a perfect game of checkers, and in the process opened the door for algorithms capable of searching vast databases of information;
  • 2011 IBM’s Watson wins Jeopardy against top human champions. It is currently training to provide medical advice to doctors. It is capable of mastering any domain of knowledge;
  • 2012 Google releases its Knowledge Graph, a semantic search knowledge base, likely to be the first step toward true artificial intelligence;
  • 2013 Facebook releases Graph Search, a semantic search engine with intimate knowledge about Facebook’s users, essentially making it impossible for us to hide anything from the intelligent algorithms;
  • 2013 BRAIN initiative aimed at reverse engineering the human brain receives 3 billion US dollars in funding by the White House, following an earlier billion euro European initiative to accomplish the same;
  • 2014 Chatbot convinced 33% of the judges that it was human and by doing so passed a restricted version of a Turing Test;
  • 2015 Single piece of general software learns to outperform human players in dozens of Atari video games;
  • 2016 Go playing deep neural network beats world champion.

Source: Artificial Intelligence Safety and Cybersecurity: a Timeline of AI Failures, https://arxiv.org/pdf/1610.07997.pdf

However, little information is shared on the failures in AI and even less on why they fail.

Failure is king

Failure is at the core of human advancement. For example, the microwave’s invention was a failed attempt at making a military grade radar during WW2. Percy Spencer noticed a melting chocolate bar in his pocket while working on magnetrons for Raytheon, a major U.S. defense contractor.

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Data and AI: The Reason Why They Can’t Live Apart

In this article, we will discuss the importance of data and their analysis, in order to design efficient and effective artificial intelligence (AI). But first of all, let’s set up an example to help us understand.

A typical problem to solve for a company is to anticipate the needs of their customers. For example, an insurer wants to know which insurance coverage would be most suitable for his client. Several factors can consciously or unconsciously influence the client’s choice of insurance (and also insurer!): Age, education, health, current and future financial situation, short and long-term objectives and so on! With the ability to serve a wide range of clients, it can become difficult for the insurer to have in mind all possible customer scenarios and recommend the appropriate product. Consequently, an AI could be conceived to handle this kind of situation. Assisting the insurer, it could quickly identify the type of client and suggest which insurance and options would suit him best.

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A Guide to Working Remotely, as a Team.

How half our team traveled abroad for two weeks and still stayed productive.


We’re no strangers to working remote.

Back in October, I worked from Banff, Alberta because I am passionate about climbing mountains. (Summits are awesome!)

Our Branding Goddess and Design Adventures, Manon, spent 4 months working remotely while traveling the world last year. She wrote all about it in this great guide.

#AwesomeJobTitles

This year, we pushed the concept further. Eight of us moved to Puerto Rico for two weeks and still managed to produce great results. Here is how we did it and what we learned.

Continue reading “A Guide to Working Remotely, as a Team.”

An Introduction to SVG – Part 3

Hey guys, welcome to another post about SVG. So far we’ve seen how to create, stylize, and load SVG shapes and we took a look at the SVG coordinate systems, the viewport and the viewbox. By now, you have acquired some basic knowledge on SVG and you might have ideas or little tricks of your own.

Continue reading “An Introduction to SVG – Part 3”

Introduction to Recommender Systems

Many receive advice, only the wise profit from it.” – Harper Lee

Many of us see recommender systems as mysterious entities that seem to know our thoughts. Just think of Netflix’s recommendation engine which suggests us movies or Amazon which suggests what products we should buy. Since their inception, these tools have been improved and refined to continuously improve user experience. Although many of them are very complex systems, the fundamental idea behind them remains very simple.

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