Algorithms are the essence of data mining and machine learning – the two processes 60% of organizations utilize to streamline their operations. Businesses can choose from several algorithms to polish their workflows, but the decision tree algorithm might be the most common.
This algorithm is all about simplicity. It branches out in multiple directions, just like trees, and determines whether something is true or false. In turn, data scientists and machine learning professionals can further dissect the data and help key stakeholders answer various questions.
This only scratches the surface of this algorithm – but it’s time to delve deeper into the concept. Let’s take a closer look at the decision tree machine learning algorithm, its components, types, and applications.
What Is Decision Tree Machine Learning?
The decision tree algorithm in data mining and machine learning may sound relatively simple due to its similarities with standard trees. But like with conventional trees, which consist of leaves, branches, roots, and many other elements, there’s a lot to uncover with this algorithm. We’ll start by defining this concept and listing the main components.
Definition of Decision Tree
If you’re a college student, you learn in two ways – supervised and unsupervised. The same division can be found in algorithms, and the decision tree belongs to the former category. It’s a supervised algorithm you can use to regress or classify data. It relies on training data to predict values or outcomes.
Components of Decision Tree
What’s the first thing you notice when you look at a tree? If you’re like most people, it’s probably the leaves and branches.
The decision tree algorithm has the same elements. Add nodes to the equation, and you have the entire structure of this algorithm right in front of you.
- Nodes – There are several types of nodes in decision trees. The root node is the parent of all nodes, which represents the overriding message. Chance nodes tell you the probability of a certain outcome, whereas decision nodes determine the decisions you should make.
- Branches – Branches connect nodes. Like rivers flowing between two cities, they show your data flow from questions to answers.
- Leaves – Leaves are also known as end nodes. These elements indicate the outcome of your algorithm. No more nodes can spring out of these nodes. They are the cornerstone of effective decision-making.
Types of Decision Trees
When you go to a park, you may notice various tree species: birch, pine, oak, and acacia. By the same token, there are multiple types of decision tree algorithms:
- Classification Trees – These decision trees map observations about particular data by classifying them into smaller groups. The chunks allow machine learning specialists to predict certain values.
- Regression Trees – According to IBM, regression decision trees can help anticipate events by looking at input variables.
Decision Tree Algorithm in Data Mining
Knowing the definition, types, and components of decision trees is useful, but it doesn’t give you a complete picture of this concept. So, buckle your seatbelt and get ready for an in-depth overview of this algorithm.
Overview of Decision Tree Algorithms
Just as there are hierarchies in your family or business, there are hierarchies in any decision tree in data mining. Top-down arrangements start with a problem you need to solve and break it down into smaller chunks until you reach a solution. Bottom-up alternatives sort of wing it – they enable data to flow with some supervision and guide the user to results.
Popular Decision Tree Algorithms
- ID3 (Iterative Dichotomiser 3) – Developed by Ross Quinlan, the ID3 is a versatile algorithm that can solve a multitude of issues. It’s a greedy algorithm (yes, it’s OK to be greedy sometimes), meaning it selects attributes that maximize information output.
- 5 – This is another algorithm created by Ross Quinlan. It generates outcomes according to previously provided data samples. The best thing about this algorithm is that it works great with incomplete information.
- CART (Classification and Regression Trees) – This algorithm drills down on predictions. It describes how you can predict target values based on other, related information.
- CHAID (Chi-squared Automatic Interaction Detection) – If you want to check out how your variables interact with one another, you can use this algorithm. CHAID determines how variables mingle and explain particular outcomes.
Key Concepts in Decision Tree Algorithms
No discussion about decision tree algorithms is complete without looking at the most significant concept from this area:
As previously mentioned, decision trees are like trees in many ways. Conventional trees branch out in random directions. Decision trees share this randomness, which is where entropy comes in.
Entropy tells you the degree of randomness (or surprise) of the information in your decision tree.
A decision tree isn’t the same before and after splitting a root node into other nodes. You can use information gain to determine how much it’s changed. This metric indicates how much your data has improved since your last split. It tells you what to do next to make better decisions.
Mistakes can happen, even in the most carefully designed decision tree algorithms. However, you might be able to prevent errors if you calculate their probability.
Enter the Gini index (Gini impurity). It establishes the likelihood of misclassifying an instance when choosing it randomly.
You don’t need every branch on your apple or pear tree to get a great yield. Likewise, not all data is necessary for a decision tree algorithm. Pruning is a compression technique that allows you to get rid of this redundant information that keeps you from classifying useful data.
Building a Decision Tree in Data Mining
Growing a tree is straightforward – you plant a seed and water it until it is fully formed. Creating a decision tree is simpler than some other algorithms, but quite a few steps are involved nevertheless.
Data preparation might be the most important step in creating a decision tree. It’s comprised of three critical operations:
Data cleaning is the process of removing unwanted or unnecessary information from your decision trees. It’s similar to pruning, but unlike pruning, it’s essential to the performance of your algorithm. It’s also comprised of several steps, such as normalization, standardization, and imputation.
Time is money, which especially applies to decision trees. That’s why you need to incorporate feature selection into your building process. It boils down to choosing only those features that are relevant to your data set, depending on the original issue.
The procedure of splitting your tree nodes into sub-nodes is known as data splitting. Once you split data, you get two data points. One evaluates your information, while the other trains it, which brings us to the next step.
Training the Decision Tree
Now it’s time to train your decision tree. In other words, you need to teach your model how to make predictions by selecting an algorithm, setting parameters, and fitting your model.
Selecting the Best Algorithm
There’s no one-size-fits-all solution when designing decision trees. Users select an algorithm that works best for their application. For example, the Random Forest algorithm is the go-to choice for many companies because it can combine multiple decision trees.
How far your tree goes is just one of the parameters you need to set. You also need to choose between entropy and Gini values, set the number of samples when splitting nodes, establish your randomness, and adjust many other aspects.
Fitting the Model
If you’ve fitted your model properly, your data will be more accurate. The outcomes need to match the labeled data closely (but not too close to avoid overfitting) if you want relevant insights to improve your decision-making.
Evaluating the Decision Tree
Don’t put your feet up just yet. Your decision tree might be up and running, but how well does it perform? There are two ways to answer this question: cross-validation and performance metrics.
Cross-validation is one of the most common ways of gauging the efficacy of your decision trees. It compares your model to training data, allowing you to determine how well your system generalizes.
Several metrics can be used to assess the performance of your decision trees:
This is the proximity of your measurements to the requested values. If your model is accurate, it matches the values established in the training data.
By contrast, precision tells you how close your output values are to each other. In other words, it shows you how harmonized individual values are.
Recall is the number of data samples in the desired class. This class is also known as the positive class. Naturally, you want your recall to be as high as possible.
F1 score is the median value of your precision and recall. Most professionals consider an F1 of over 0.9 a very good score. Scores between 0.8 and 0.5 are OK, but anything less than 0.5 is bad. If you get a poor score, it means your data sets are imprecise and imbalanced.
Visualizing the Decision Tree
The final step is to visualize your decision tree. In this stage, you shed light on your findings and make them digestible for non-technical team members using charts or other common methods.
Applications of Decision Tree Machine Learning in Data Mining
The interest in machine learning is on the rise. One of the reasons is that you can apply decision trees in virtually any field:
- Customer Segmentation – Decision trees let you divide customers according to age, gender, or other factors.
- Fraud Detection – Decision trees can easily find fraudulent transactions.
- Medical Diagnosis – This algorithm allows you to classify conditions and other medical data with ease using decision trees.
- Risk Assessment – You can use the system to figure out how much money you stand to lose if you pursue a certain path.
- Recommender Systems – Decision trees help customers find their next product through classification.
Advantages and Disadvantages of Decision Tree Machine Learning
- Easy to Understand and Interpret – Decision trees make decisions almost in the same manner as humans.
- Handles Both Numerical and Categorical Data – The ability to handle different types of data makes them highly versatile.
- Requires Minimal Data Preprocessing – Preparing data for your algorithms doesn’t take much.
- Prone to Overfitting – Decision trees often fail to generalize.
- Sensitive to Small Changes in Data – Changing one data point can wreak havoc on the rest of the algorithm.
- May Not Work Well with Large Datasets – Naïve Bayes and some other algorithms outperform decision trees when it comes to large datasets.
Possibilities are Endless With Decision Trees
The decision tree machine learning algorithm is a simple yet powerful algorithm for classifying or regressing data. The convenient structure is perfect for decision-making, as it organizes information in an accessible format. As such, it’s ideal for making data-driven decisions.
If you want to learn more about this fascinating topic, don’t stop your exploration here. Decision tree courses and other resources can bring you one step closer to applying decision trees to your work.
Soon, we will be launching four new Degrees for AY24-25 at OPIT – Open Institute of Technology
I want to offer a behind-the-scenes look at the Product Definition process that has shaped these upcoming programs.
🚀 Phase 1: Discovery (Late May – End of July)
Our journey began with intensive brainstorming sessions with OPIT’s Academic Board (Francesco Profumo, Lorenzo Livi, Alexiei Dingli, Andrea Pescino, Rosario Maccarrone) . We also conducted 50+ interviews with tech and digital entrepreneurs (both from startups and established firms), academics and students. Finally, we deep-dived into the “Future of Jobs 2023” report by the World Economic Forum and other valuable research.
🔍 Phase 2: Selection – Crafting Our Roadmap (July – August)
Our focus? Introducing new degrees addressing critical workforce shortages and upskilling/reskilling needs for the next 5-10 years, promising significant societal impact and a broad market reach.
Our decision? To channel our energies on full BScs and MScs, and steer away from shorter courses or corporate-focused offerings. This aligns perfectly with our core mission.
💡 Focus Areas Unveiled!
We’re thrilled to concentrate on pivotal fields like:
- Advanced AI
- Digital Business
- Metaverse & Gaming
- Cloud Computing (less “glamorous”, but market demand is undeniable).
🎓 Phase 3: Definition – Shaping the Degrees (August – November)
With an expert in each of the above fields, and with the strong collaboration of our Academic Director, Prof. Lorenzo Livi , we embarked on a rigorous “drill-down process”. Our goal? To meld modern theoretical knowledge with cutting-edge competencies and skills. This phase included interviewing over 60+ top academics, industry professionals, and students and get valuable, program-specific, insights from our Marketing department.
🌟 Phase 4: Accreditation and Launch – The Final Stretch
We’re currently in the accreditation process, gearing up for the launch. The focus is now shifting towards marketing, working closely with Greta Maiocchi and her Marketing and Admissions team. Together, we’re translating our new academic offering into a compelling value proposition for the market.
Stay tuned for more updates!
Far from being a temporary educational measure that came into its own during the pandemic, online education is providing students from all over the world with new ways to learn. That’s proven by statistics from Oxford Learning College, which point out that over 100 million students are now enrolled in some form of online course.
The demand for these types of courses clearly exists.
In fact, the same organization indicates that educational facilities that introduce online learning see a 42% increase in income – on average – suggesting that the demand is there.
Enter the Open Institute of Technology (OPIT).
Delivering three online courses – a Bachelor’s degree in computer science and two Master’s degrees – with more to come, OPIT is positioning itself as a leader in the online education space. But why is that? After all, many institutions are making the jump to e-learning, so what separates OPIT from the pack?
Here, you’ll discover the answers as you delve into the five reasons why you should trust OPIT for your online education.
Reason 1 – A Practical Approach
OPIT focuses on computer science education – a field in which theory often dominates the educational landscape. The organization’s Rector, Professor Francesco Profumo, makes this clear in a press release from June 2023. He points to a misalignment between what educators are teaching computer science students and what the labor market actually needs from those students as a key problem.
“The starting point is the awareness of the misalignment,” he says when talking about how OPIT structures its online courses. “That so-called mismatch is generated by too much theory and too little practical approach.” In other words, students in many classes spend far too much time learning the “hows” and “whys” behind computerized systems without actually getting their hands dirty with real work that gives them practical experience in using those systems.
OPIT takes a different approach.
It has developed a didactic approach that focuses far more on the practical element than other courses. That approach is delivered through a combination of classroom sessions – such as live lessons and masterclasses – and practical work offered through quizzes and exercises that mimic real-world situations.
An OPIT student doesn’t simply learn how computers work. They put their skills into practice through direct programming and application, equipping them with skills that are extremely attractive to major employers in the tech field and beyond.
Reason 2 – Flexibility Combined With Support
Flexibility in how you study is one of the main benefits of any online course.
You control when you learn and how you do it, creating an environment that’s beneficial to your education rather than being forced into a classroom setting with which you may not feel comfortable. This is hardly new ground. Any online educational platform can claim that it offers “flexibility” simply because it provides courses via the web.
Where OPIT differs is that it combines that flexibility with unparalleled support bolstered by the experiences of teachers employed from all over the world. The founder and director of OPIT, Riccardo Ocleppo, sheds more light on this difference in approach when he says, “We believe that education, even if it takes place physically at a distance, must guarantee closeness on all other aspects.” That closeness starts with the support offered to students throughout their entire study period.
Tutors are accessible to students at all times. Plus, every participant benefits from weekly professor interactions, ensuring they aren’t left feeling stuck on an educational “island” and have to rely solely on themselves for their education. OPIT further counters the potential isolation that comes with online learning with a Student Support team to guide students through any difficulties they may have with their courses.
In this focus on support, OPIT showcases one of its main differences from other online platforms.
You don’t simply receive course material before being told to “get on with it.” You have the flexibility to learn at your own pace while also having a support structure that serves as a foundation for that learning.
Reason 3 – OPIT Can Adapt to Change Quickly
The field of computer science is constantly evolving.
In the 2020s alone, we’ve seen the rise of generative AI – spurred on by the explosive success of services like ChatGPT – and how those new technologies have changed the way that people use computers.
Riccardo Ocleppo has seen the impact that these constant evolutions have had on students. Before founding OPIT, he was an entrepreneur who received first-hand experience of the fact that many traditional educational institutions struggle to adapt to change.
“Traditional educational institutions are very slow to adapt to this wave of new technologies and trends within the educational sector,” he says. He points to computer science as a particular issue, highlighting the example of a board in Italy of which he is a member. That board – packed with some of the country’s most prestigious tech universities – spent three years eventually deciding to add just two modules on new and emerging technologies to their study programs.
That left Ocleppo feeling frustrated.
When he founded OPIT, he did so intending to make it an adaptable institution in which courses were informed by what the industry needs. Every member of its faculty is not only a superb teacher but also somebody with experience working in industry. Speaking of industry, OPIT collaborates with major companies in the tech field to ensure its courses deliver the skills that those organizations expect from new candidates.
This confronts frustration on both sides. For companies, an OPIT graduate is one for which they don’t need to bridge a “skill gap” between what they’ve learned and what the company needs. For you, as a student, it means that you’re developing skills that make you a more desirable prospect once you have your degree.
Reason 4 – OPIT Delivers Tier One Education
Despite their popularity, online courses can still carry a stigma of not being “legitimate” in the face of more traditional degrees. Ocleppo is acutely aware of this fact, which is why he’s quick to point out that OPIT always aims to deliver a Tier One education in the computer science field.
“That means putting together the best professors who create superb learning material, all brought together with a teaching methodology that leverages the advancements made in online teaching,” he says.
OPIT’s degrees are all accredited by the European Union to support this approach, ensuring they carry as much weight as any other European degree. It’s accredited by both the European Qualification Framework (EQF) and the Malta Qualification Framework (MQF), with all of its courses having full legal value throughout Europe.
It’s also here where we see OPIT’s approach to practicality come into play via its course structuring.
Take its Bachelor’s degree in computer science as an example.
Yes, that course starts with a focus on theoretical and foundational knowledge. Building a computer and understanding how the device processes instructions is vital information from a programming perspective. But once those foundations are in place, OPIT delivers on its promises of covering the most current topics in the field.
Machine learning, cloud computing, data science, artificial intelligence, and cybersecurity – all valuable to employers – are taught at the undergraduate level. Students benefit from a broader approach to computer science than most institutions are capable of, rather than bogging them down in theory that serves little practical purpose.
Reason 5 – The Learning Experience
Let’s wrap up by honing in on what it’s actually like for students to learn with OPIT.
After all, as Ocleppo points out, one of the main challenges with online education is that students rarely have defined checkpoints to follow. They can start feeling lost in the process, confronted with a metaphorical ocean of information they need to learn, all in service of one big exam at the end.
Alternatively, some students may feel the temptation to not work through the materials thoroughly, focusing instead on passing a final exam. The result is that those students may pass, but they do so without a full grasp of what they’ve learned – a nightmare for employers who already have skill gaps to handle.
OPIT confronts both challenges by focusing on a continuous learning methodology. Assessments – primarily practical – take place throughout the course, serving as much-needed checkpoints for evaluating progress. When combined with the previously mentioned support that OPIT offers, this approach has led to courses that are created from scratch in service of the student’s actual needs.
Choose OPIT for Your Computer Science Education
At OPIT, the focus lies as much on helping students to achieve their dream careers as it does on teaching them. All courses are built collaboratively. With a dedicated faculty combined with major industry players, such as Google and Microsoft, it delivers materials that bridge the skill gap seen in the computer science field today.
There’s also more to come.
Beyond the three degrees OPIT offers, the institution plans to add more. Game development, data science, and cloud computing, to name a few, will receive dedicated degrees in the coming months, accentuating OPIT’s dedication to adapting to the continuous evolution of the computer science industry. Discover OPIT today – your journey into computing starts with the best online education institution available.