arXiv subjects

Felipe Maia Polo

Publications and source records attributed to Felipe Maia Polo.

22 records · Page 2Linked to original sources

arXiv2021

Effects of personality traits in predicting grade retention of Brazilian students

Student's grade retention is a key issue faced by many education systems, especially those in developing countries. In this paper, we seek to gauge the relevance of students' personality traits in predicting grade retention in Brazil. For that, we used data collected in 2012 and 2017, in the city of Sertaozinho, countryside of the state of Sao Paulo, Brazil. The surveys taken in Sertaozinho included several socioeconomic questions, standardized tests, and a personality test. Moreover, students were in grades 4, 5, and 6 in 2012. Our approach was based on training machine learning models on the surveys' data to predict grade retention between 2012 and 2017 using information from 2012 or before, and then using some strategies to quantify personality traits' predictive power. We concluded that, besides proving to be fairly better than a random classifier when isolated, personality traits contribute to prediction even when using socioeconomic variables and standardized tests results.

stat.AP↗

arXiv2021

OCDE: Odds Conditional Density Estimator

Conditional density estimation (CDE) models can be useful for many statistical applications, especially because the full conditional density is estimated instead of traditional regression point estimates, revealing more information about the uncertainty of the random variable of interest. In this paper, we propose a new methodology called Odds Conditional Density Estimator (OCDE) to estimate conditional densities in a supervised learning scheme. The main idea is that it is very difficult to estimate $p_{x,y}$ and $p_{x}$ in order to estimate the conditional density $p_{y|x}$, but by introducing an instrumental distribution, we transform the CDE problem into a problem of odds estimation, or similarly, training a binary probabilistic classifier. We demonstrate how OCDE works using simulated data and then test its performance against other known state-of-the-art CDE methods in real data. Overall, OCDE is competitive compared with these methods in real datasets.

stat.ME↗

arXiv2021

Predicting Legal Proceedings Status: Approaches Based on Sequential Text Data

The objective of this paper is to develop predictive models to classify Brazilian legal proceedings in three possible classes of status: (i) archived proceedings, (ii) active proceedings, and (iii) suspended proceedings. This problem's resolution is intended to assist public and private institutions in managing large portfolios of legal proceedings, providing gains in scale and efficiency. In this paper, legal proceedings are made up of sequences of short texts called "motions." We combined several natural language processing (NLP) and machine learning techniques to solve the problem. Although working with Portuguese NLP, which can be challenging due to lack of resources, our approaches performed remarkably well in the classification task, achieving maximum accuracy of .93 and top average F1 Scores of .89 (macro) and .93 (weighted). Furthermore, we could extract and interpret the patterns learned by one of our models besides quantifying how those patterns relate to the classification task. The interpretability step is important among machine learning legal applications and gives us an exciting insight into how black-box models make decisions.

cs.CL↗

arXiv2020

Skills to not fall behind in school

Many recent studies emphasize how important the role of cognitive and social-emotional skills can be in determining people's quality of life. Although skills are of great importance in many aspects, in this paper we will focus our efforts to better understand the relationship between several types of skills with academic progress delay. Our dataset contains the same students in 2012 and 2017, and we consider that there was a academic progress delay for a specific student if he or she progressed less than expected in school grades. Our methodology primarily includes the use of a Bayesian logistic regression model and our results suggest that both cognitive and social-emotional skills may impact the conditional probability of falling behind in school, and the magnitude of the impact between the two types of skills can be comparable.

stat.AP↗