Decision tree algorithm: what is it and how is it used

Machine learning is a hot topic today. It’s more and more common to use it to design a multitude of different programs and applications, such as personalized recommendations in Netflix or Amazon. But the potential of this technology does not stop here, since more and more companies are using predictive models internally through a decision tree algorithm.

In this article, we are going to see what a decision tree algorithm is, how it works and how this type of machine learning algorithm is made. First of all, we can say that this type of algorithm is usually created using Python, one of the most important programming languages in the field of machine learning.

With this in mind, and given the current growth of this particular sector, learning to program through a Python Programmer course with a specialization in Machine Learning can be a way to enhance your chances of being employed. But we will also talk about this throughout the article. For now, let’s first focus on what a decision tree algorithm is.

 

What is a decision tree algorithm?

A decision tree algorithm is, as we’ve anticipated above, an machine learning algorithm used in predictive modeling. This type of algorithm makes predictions based on the relationships established between different input columns and prediction columns. Each of these input columns contains a number of data sets, both structured and unstructured.

This type of machine learning algorithms are programmed to identify each input column and relate it to one of the elements within the prediction columns. In order to do this, it uses a series of values, called states, which are then used to predict correlations with the input data. They are able to do this through classification or linear regression processes.

Decision tree algorithms start from a single node, called root, and then break down into different attributes, following a “two-branches” model. Different conditions are presented and the final decision (a true or false choice) is reached when the branches reach an endpoint.

Algorithms are created using programming languages such as Python, which is widely used in machine learning.

For example, a decision tree algorithm can be used to determine what kind of situations will lead a customer to buy a certain product. If nine out of ten of those who buy this product are under 25 years of age, and only 2 out of 10 are people over 40 years of age, this algorithm infers that age is a key factor when making shopping predictions. As such, the algorithm would make it possible to develop a predictive model in which age is a key factor. 

It’s also important to take into account that this type of algorithms are also able to determine division points as part of the decision tree. This happens when more than one column is defined as a predictor element. In this case, the decision tree in Python will generate an independent decision tree for each of the defined prediction columns.

 

What is a decision tree algorithm used for?

The commercial sector presents one of the clearest examples of the use of a decision tree algorithm. As we said, this algorithm can be used to create predictive models related to customer purchase behavior. As such, they can be used to identify the key points such as age, trends, gender, etc. that can be determining in purchase decisions. 

In order to do this, companies must first have access to information. As we said at the beginning, amount of data that we are currently generating with both our online activity and the use of smart devices allows companies to collect a huge amount of information about our behavior.

The decision tree is the most widely used supervised learning algorithm in machine learning.

By adding data into a large database, it’s possible to create different types of algorithms that are then employed in predicting future behavior based on input about past behaviors. The precision of this type of algorithms is varying and depends on many factors, but technology developments are translating in more reliability and, as a consequence, better chances for companies to access valuable data to improve their business models.

 

How is a decision tree algorithm in Python created and what is it used for?

A decision tree algorithm provides a graphical representation of possible solutions to a decision based on data sets. In order to do this, the first step is to import relevant resource libraries to Python, such as Scikit-learn, which includes several classification and regression algorithms for analyzing data sets.

Before starting this work, it is also important to create and organize the databases that will be used. These will serve as a starting point for the creation of the decision tree algorithm. Once you have access to the data, you can proceed to create the decision tree algorithm following a series of steps. For this, it is also crucial to establish the conditions that will determine the tree bifurcations that take place until reaching a final solution.

In order to create a decision tree in Python, it is essential to know how to program and how to use the specific libraries intended for machine learning.

Once the tree is created, we must proceed to its analysis in order to create the predictive model that is suitable for the project. While we’ve already mentioned some examples of use at the beginning, such as Netflix recommendations (the starting point being the series and genres that users have already seen), purchases on Amazon (based on the type of items we usually buy) or music on Spotify (depending on the genres and artists that we listen to the most).

 

Train and become a Python programmer!

Now you know what a decision tree algorithm is, how it works and what it is used for. It’s time to get learn how to use Python and join one of the most promising professional options within the field of programming. While there are many options within this broad field, at Tokyo School we offer a Python Programmer course with a specialization in Machine Learning that will turn you into a true algorithm master.

Do you want to learn more about us or the training we offer? Don’t hesitate! Get in touch with us through the form below and start your training to become a specialized Python programmer! Take the next step and become a Tokyer. We can’t wait to meet you!