## 1 Introduction
One can think of binary classification algorithms as something similar to teaching a kid to distinguish between a cat and a dog. We can present to the kid several images of cats and dogs while telling him, for each of the images, if the animal in there is a cat or a dog until he learns to distinguish them based on their characteristics. After that, when the kid sees either a cat or a dog in the street, he would be capable of telling the name of the animal. In this example, the name of the corresponding animal in the photos is what is called the \"label\" of the input data. As simple as the previous example may be, there is a lot of mathematics, linear algebra, and statistics behind the algorithms that allow a computer to do that type of classification. For that reason, it is very important for Education 4.0 to provide future scientists and engineers with understandable information about the basis of ML, as well as with a starting point where they can test the behavior of Machine Learning algorithms and verify their correctness while developing competencies and skills that are required for Industry 4.0 progress. One way to offer this starting point is to provide students with the possibility of testing the algorithms over a test bench so that they can acquire knowledge about the development of the experiments while understanding the different results. Developing a test bench as teaching/learning material is a way in which schools may accomplish one of the main goals of the Education 4.0 paradigm, which is to give students the opportunity to apply the acquired knowledge and interpret the outcome of its application through the construction of real-life scenarios [29]. Thus, the main objective of this paper is for it to be useful as teaching and learning material in the Machine Learning curriculum of Education 4.0 so that students can acquire expertise on disruptive technologies while developing competencies such as design mindset, transdisciplinary knowledge, and computational skills. This objective is addressed through an explanation of the basic theory behind three binary classification algorithms: K-Nearest-Neighbors (KNN), Linear Discriminant Analysis (LDA), and Simple Perceptron. On one hand, the first two algorithms were chosen because of their relative simplicity compared to more advanced ML algorithms, as well as their several applications in real-life Industry 4.0 situations. On the other hand, the perceptron was chosen because it constitutes the basis of artificial neural networks, which are currently used in a lot of areas that are under the scope of the fourth industrial revolution. Moreover, in order for this paper to be used in the full scope of the Education 4.0 paradigm, we present a transdisciplinary test bench so that students can learn how to use the algorithms and how to interpret the results through experimentation. The presented test bench consists on three different datasets: Cleveland Heart Disease dataset [30], Banknote Authentication dataset [31], and Wisconsin Cancer dataset [31]. At the time of writing this paper, we have not found other work that provides an introduction to these algorithms and that explains how to apply them over real-life situations under the Education 4.0 framework. This article first goes through a general review of the three algorithms mentioned above so that students can learn the basics of each of them. After that, a bibliographic comparison about their advantages and disadvantages is carried out. Moreover, a test of their performance and behavior while working on the given datasets is done to show students which one is superior for these specific case studies. Finally, we conclude by pointing out the main takeaways of this work, and we give an idea of how the content of this paper can be used by teachers in class to enhance and validate the understanding of this topic by students.