# Random forest Algorithm for the Classification of Spectral Data of Astronomical Objects
## 1 Introduction  
Since ancient times, human beings have been fascinated by observing the skies [1]. The cultural manifestations of great civilizations attest to the precision with which peoples such as the Maya recorded astronomical events [2]. The notable results of these observations have undeniably brought usefulness to the daily lives of inhabitants, such as calendars, numerical systems, agriculture, and social life, among other activities in which their impact has been measured [3]. Over the years, centuries, and millennia, human beings have built increasingly large astronomical observatories to increase the number of discoveries related to celestial objects. The [PERSON] telescope and the photographs it captures of celestial objects are clear examples of these impressive advances [4].  
This development in astronomical observation technology has led to a noticeable increase in the number of collected elements, which far exceeds human capacity to analyze findings. For this reason, researchers must now turn to machine learning to analyze such data, identifying and classifying transient objects or events within extensive observations of the firmament [5].  
In this context, the classification of celestial objects has been a topic of great interest for astronomers and astrophysicists for decades. With the increase in the size and complexity of astronomical datasets, the use of machine learning algorithms has become essential for classifying objects based on their spectral data [6].  
On the other hand, the world is witnessing the evolution of new scientific ideas that provide theoretical support for pattern recognition, machine learning, and related areas. At the same time, machine learning algorithms are becoming closer to interestingapplications in all sciences, including astronomy. In machine learning, there are four basic tasks corresponding to two paradigms. The unsupervised paradigm includes the clustering task, while the remaining three tasks belong to the supervised paradigm: classification, recalling, and regression [7].  
In the state of the art, there are several important approaches whose algorithms perform the task of pattern classification. Among the most important, we can mention the Bayesian approach [8], the nearest neighbors-based approach [9], support vector machines [10; 11], and neural networks, especially multi-layer perceptron [12], and the whole range of algorithms based on deep learning [13].  
Several authors have studied astronomical topics using machine-learning techniques to classify astronomical objects. It is worth mentioning [14] that the authors conduct an interesting comparative study of different pattern classification algorithms applied to astronomical datasets. In [15], the authors incorporate unsupervised algorithms (clustering) into astronomical data and conduct a comparative study with pattern classification algorithms.  
In this article, we propose the use of one of the most successful approaches in the classification of astronomical objects. This approach involves decision trees [16], especially when implemented as random forests [17], which are ensembles of decision trees. This is particularly relevant, given that recent studies have compared the performance of some random forest algorithms' performance with deep learning in classifying astronomical objects [18]. Of particular interest to the authors of this manuscript are studies that use datasets of astronomical objects extracted from the Sloan digital sky survey (SDSS) [19].  
The rest of the paper is organized as follows: Section 2 consists of two subsections, where the datasets and the random forest algorithm are described. Section 3 includes the classification algorithms against which our proposal will be compared. Additionally, the experimental results are presented and discussed. Finally, in Section 4, the conclusions are provided.