Due to their versatility, concept maps are used in various educational settings and serve as tools that enable educators to comprehend students' knowledge construction. An essential component for analyzing a concept map is its structure, which can be categorized into three distinct types: spoke, network, and chain. Understanding the predominant structure in a map offers insights into the student's depth of comprehension of the subject. Therefore, this study examined 317 distinct concept map structures, classifying them into one of the three types, and used statistical and descriptive information from the maps to train multiclass classification models. As a result, we achieved an 86\% accuracy in classification using a Decision Tree. This promising outcome can be employed in concept map assessment systems to provide real-time feedback to the student.

PyTorch library is for deep learning. Some applications of deep learning models are to solve regression or classification problems. In this tutorial, you will discover how to use PyTorch to develop and evaluate neural network models for multi-class classification problems. In this tutorial, you will use a standard machine learning dataset called the iris flowers dataset. It is a well-studied dataset and good for practicing machine learning.

Machine Learning Models play a vital part in Artificial Intelligence. In simple words, they are mathematical representations. In other words, they are the output we receive after training a process. What a machine learning model does is discovers the patterns in a training dataset. In other words, machine learning models map inputs to the outputs of the given dataset.