Decision Tree Algorithm: What It Is and How It Is Used
Machine learning is a hot topic today. It is increasingly being used for developing a variety of software and applications, such as personalized recommendations on Netflix or Amazon. But the potential of this technology does not end there, as more and more companies are using predictive models internally based on the decision tree algorithm.
In this article, we will look at what the decision tree algorithm is, how it works, and how this type of machine learning algorithm is structured. First of all, it can be said that this type of algorithm is typically created using Python, one of the most important programming languages in the field of machine learning.
Given this, as well as the current growth of this industry, learning to program in Python courses with a specialization in Machine Learning can improve your job prospects. But we will talk more about this later in the article. For now, let's focus on what the decision tree algorithm is.
What is the Decision Tree Algorithm?
The decision tree algorithm is, as we assumed above, a machine learning algorithm used in predictive modeling. This type of algorithm builds predictions based on relationships established between different columns of input data and prediction columns. Each of these columns contains several datasets, both structured and unstructured.
Machine learning algorithms of this type are programmed to identify each column of input data and link it to one of the elements in the prediction columns. To do this, they use a set of values called states, which are then used to predict correlations with the input data. They use classification or linear regression processes for this purpose.
Decision tree algorithms start with a single node called the root, and then split into various attributes following a "two-branch" model. Different conditions are presented, and the final decision (true or false choice) is made when the branches reach an endpoint.
To create algorithms, programming languages such as Python, which is widely used in machine learning, are employed.
For example, a decision tree algorithm can be used to determine which situations will lead a customer to purchase a particular product. If nine out of ten buyers of that product are people under 25, and only 2 out of 10 are people over 40, then the algorithm concludes that age is a key factor in predicting purchases. Thus, the algorithm allows the development of a predictive model where age is the key factor.
It is also important to note that algorithms of this type are capable of determining split points within the decision tree. This occurs when more than one column is defined as a predictor element. In this case, a decision tree in Python will generate an independent decision tree for each of the specified prediction columns.
What is the Decision Tree Algorithm Used For?
One of the most striking examples of using the decision tree algorithm is in the commercial sector. As we have said, this algorithm can be used to create predictive models related to consumer behavior. As such, they can be used to identify key factors such as age, trends, gender, etc., which may be decisive in the purchase decision.
To do this, companies must first gain access to information. As we mentioned at the beginning, the volume of data we currently generate both from our online activity and from using smart devices allows companies to collect vast amounts of information about our behavior.
The decision tree is the most widely used supervised learning algorithm in machine learning.
By aggregating data into a large database, various types of algorithms can be created, which are then used to predict future behavior based on past behavior. The accuracy of these types of algorithms varies and depends on many factors, but technological advancements are leading to increased reliability and, consequently, greater opportunities for companies to access valuable data to improve their business models.
How Is the Decision Tree Algorithm Created and Used in Python?
The decision tree algorithm is a graphical representation of possible solution options based on a dataset. To do this, you first need to import the appropriate resource libraries into Python, such as Scikit-learn, which includes several classification and regression algorithms for analyzing datasets.
Before starting, it is also important to create and organize the databases that will be used. They will serve as the starting point for creating the decision tree algorithm. With access to the data, you can proceed to create the decision tree algorithm by following a series of steps. It is also very important to set the conditions that will determine the tree's bifurcations occurring before reaching the final decision.
To create a decision tree in Python, you need to know how to program and how to use special libraries designed for machine learning.
Once the tree is created, you need to analyze it to create a predictive model suitable for the project. At the very beginning, we already mentioned some examples of use, such as Netflix recommendations (starting from the shows and genres that users have already watched), Amazon purchases (based on the type of products we typically buy), or music on Spotify (depending on the genres and artists we listen to most often).