CoronaBot - explainable AI chatbot for COVID19 epidemic
Welcome to CoronaBot - explainable AI chatbot for COVID19 mortality prediction. You can talk with the Machine Learning model and ask for predictions and their explanations.
xAI chatbot for Titanic survival
This chatbot enables human interaction with a Machine Learning model trained on Titanic dataset. It answers various questions about the model and explains its predictions.
pyCeterisParibus - explaining Machine Learning models in Python
pyCeterisParibus is a Python library based on an R package CeterisParibus. It implements Ceteris Paribus Plots. They allow understanding how the model response would change if a selected variable is changed. It’s a perfect tool for What-If scenarios. Ceteris Paribus is a Latin phrase meaning all else unchanged. These plots present the change in model response as the values of one feature change with all others being fixed. Ceteris Paribus method is model-agnostic - it works for any Machine Learning model.
Modelling response to trypophobia trigger using intermediate layers of ImageNet networks
In this project, we approach the problem of detecting trypophobia triggers using Convolutional neural networks. We show that standard architectures such as VGG or ResNet are capable of recognizing trypophobia patterns. We also conduct experiments to analyze the nature of this phenomenon. To do that, we dissect the network decreasing the number of its layers and parameters. We prove, that even significantly reduced networks have accuracy above 91% and focus their attention on the trypophobia patterns as presented on the visual explanations.