Tag: Machine Learning

Difficult Targets to Optimize: the ROC AUC

In many binary classification problems, especially in domains with highly unbalanced problems (such as the medical domain and rare event detection), we need to make sure our model does not become too biased for the more predominant class.  Thus, you may have heard that accuracy is not a good metric to validate classifiers in unbalanced […]

Written by on Dec 18, 2020

Explainable AI in Healthcare

Transparency is of utmost importance when AI is applied to high stake decision problems where additional information on the underlying process beyond the output of the model may be required. Taking the automation of loan attribution as an example, a client that has a loan denied will surely want to know why did that happen […]

Written by on Nov 24, 2020

Fighting cervical cancer with Artificial Intelligence

In case you missed it. You may find below our webinar on the Symposium on Bioengineering 2020 at FEUP. Also, we reply to some answers that we didn’t have time to answer during the talk. Abstract Despite being easily preventable, cervical cancer remains a leading cause of death among women — the main reason behind […]

Written by on Aug 25, 2020

A balance between two unbalanced options, representing the potential imbalance an AI algorithm generates

Fairness in AI

In collaboration with Data Science for Social Good Portugal, we are excited to announce a groundbreaking series of webinars focusing on AI topics that intersect with the greater social good. Our recent webinar, the second installment in this series, took place on the 29th of July and featured an insightful presentation by Francisca Morgado. She […]

Written by on Aug 18, 2020

Applying geospatial data for Machine Learning, with a focus on social good

In partnership with Data Science for Social Good Portugal, we are launching a series of webinars in AI topics related to social good. The first talk was by Paulo Maia, on the 28th of June, with the topic “Applying geospatial data for Machine Learning, with a focus on social good”. In case you weren’t able […]

Written by on Jul 16, 2020

Experiment Management and Reproducible Research

In this tutorial, we will discuss how can we achieve reproducible data pipelines and research while keeping track of the experiments that lead to reproducible production Machine Learning models. We will go over all the popular tools we use available and do a small demo of how we can use these tools (e.g., DVC, Pachiderm, […]

Written by on Jul 13, 2020

A leaking faucet showing a careless management of water sources, typical behavior on fraud detection

Who is tampering your meters? Fraud detection in Utilities

Meter tampering is a common threat to the business side of utility services and a public security threat, incurring uncontrolled tweaks that may increase the risk of accidents. So, fraud detection is core for any utility service company. The presence of users that manipulate the network typically translates to an increased service price for the […]

Written by on Jun 19, 2020

Embedding Domain Knowledge for Estimating Customer Lifetime Value

As part of the rise of Deep Neural Networks in the ML community, we have observed an increasing fit-predict approach, where AI practitioners don’t take the time to think about the domain knowledge that is already available and how to embed that knowledge in the models. In this blogpost, we will cover how we created […]

Written by on Apr 6, 2020

Appendix: Embedding Domain Knowledge for CLV Estimation

This is an appendix to the blog post Embedding Domain Knowledge for Estimating Customer Lifetime Value. We will describe some alternatives we considered for solving the proposed problem, but did not end up being implemented. First, let’s assume we have a pre-trained model for estimating the probability of the target and . Estimating Lifetime Value using […]

Written by on Apr 6, 2020