A Graphical User Interface for Fast Evaluation and Testing of Machine Learning Models Performance
Leonel Rosasn-Arias, Gabriel Sanchezn-Perez, Karina Toscano, Hector Manuel Perez-Meana, José Portillo-Portillo · 2019
In this paper we propose the design of a graphical tool for fast evaluation of Machine Learning (ML) models performance in classification tasks. The motivation behind this work is to get some intuition on what machine learning model we can use to get the best possible outcome out of our datasets. The designed GUI allows us to decide whether applying data standardization and applying different data dimensionality reduction algorithms based on Principal Component Analysis (PCA). Also, we can choose between 6 generative and discriminative supervised ML classifiers for making the final predictions, including: Logistic Regression, Support Vector Machines, Random Forest, K-nearest Neighbors, Gaussian Naive Bayes and Neural Network (Multilayer Perceptron). Results demonstrate that we are able to effectively apply this set of algorithms to any given dataset that satisfies our system requirements and also visualize the model behavior as well as its performance metrics.