Measuring students’ progress in Machine Learning: A case study of Decision Trees and Random Forests

Machine Learning (ML) is a rapidly growing field, therefore ensuring that students deeply understand such concepts is of key importance in order to certify that they are prepared for the challenges and opportunities of the future workforce. Despite this, literature on teaching ML and assessing students' understanding with regard to this field is...

Optimal Robust Decision Trees: A dynamic programming approach

Decision trees are integral to machine learning, with their robustness being a critical measure of effectiveness against adversarial data manipulations. Despite advancements in algorithms, current solutions are either optimal but lack scalability or scale well, but do not guarrantee optimality. This paper presents a novel adaptation of the...

Optimal decision trees for the Algorithm Selection Problem: A dynamic programming approach

The Algorithm Selection Problem is a relevant question in computer science that would enable us to predict which algorithm would perform better on a given instance of a problem. <br/>Different solutions have been proposed, either using Mixed Integer Programming or machine learning models, but both suffer from either poor scalability, no...

Optimal Regression Trees via Dynamic Programming: Optimization techniques for learning Regression Trees

Decision trees make decisions in a way interpretable to humans, this is important when machines are increasingly used to aid in making high-stakes and socially sensitive decisions. While heuristics have been used for a long time to find decision trees with reasonable accuracy, recent approaches find fully optimal trees. Due to the computational...

SurTree: constructing optimal survival trees with MurTree

Survival analysis revolves around studying and predicting the time it takes for a particular event to occur. In clinical trials on terminal illnesses, this is usually the time from the diagnosis of a patient until their death. Estimating the odds of survival of a new patient can be done by analyzing survival data from past patients in similar...

Using Decision Trees produced by Generative Adversarial Imitation Learning to give insight into black box Reinforcement Learning models

Machine learning models are increasingly being used in fields that have a direct impact on the lives of humans. Often these machine learning models are black-box models and they lack transparency and trust which is holding back the implementation. To increase transparency and trust this research investigates whether imitation learning,...