Application of a virtual scientific experiment model in different educational contexts

E-learning practice is continuously using experimentation in order to enhance the basic information transfer model where knowledge is passed from the system/ tutors to the students. Boosting student productivity through on-line experimentation is not simple since many organizational, educational and technological issues need to be dealt with. This work describes the application of a Learning Model for Virtual Scientific Experiments (VSEs) in two different scenarios: Information and Communication Technologies and Physics. As part of the first, a VSE for Wireless Sensor Networks was specified and deployed while the second involved the specification and design of a collaborative VSE for physics experiments. Preliminary implementation and deployment results are also discussed

Inducing Interpretable Voting Classifiers without Trading Accuracy for Simplicity: Theoretical Results, Approximation Algorithms

Recent advances in the study of voting classification algorithms have brought empirical and theoretical results clearly showing the discrimination power of ensemble classifiers. It has been previously argued that the search of this classification power in the design of the algorithms has marginalized the need to obtain interpretable classifiers. Therefore, the question of whether one might have to dispense with interpretability in order to keep classification strength is being raised in a growing number of machine learning or data mining papers. The purpose of this paper is to study both theoretically and empirically the problem. First, we provide numerous results giving insight into the hardness of the simplicity-accuracy tradeoff for voting classifiers. Then we provide an efficient "top-down and prune" induction heuristic, WIDC, mainly derived from recent results on the weak learning and boosting frameworks. It is to our knowledge the first attempt to build a voting classifier as a base formula using the weak learning framework (the one which was previously highly successful for decision tree induction), and not the strong learning framework (as usual for such classifiers with boosting-like approaches). While it uses a well-known induction scheme previously successful in other classes of concept representations, thus making it easy to implement and compare, WIDC also relies on recent or new results we give about particular cases of boosting known as partition boosting and ranking loss boosting. Experimental results on thirty-one domains, most of which readily available, tend to display the ability of WIDC to produce small, accurate, and interpretable decision committees

Tree Boosting Data Competitions with XGBoost

This Master's Degree Thesis objective is to provide understanding on how to approach a supervised learning predictive problem and illustrate it using a statistical/machine learning algorithm, Tree Boosting. A review of tree methodology is introduced in order to understand its evolution, since Classification and Regression Trees, followed by Bagging, Random Forest and, nowadays, Tree Boosting. The methodology is explained following the XGBoost implementation, which achieved state-of-the-art results in several data competitions. A framework for applied predictive modelling is explained with its proper concepts: objective function, regularization term, overfitting, hyperparameter tuning, k-fold cross validation and feature engineering. All these concepts are illustrated with a real dataset of videogame churn; used in a datathon competition