Tag: Machine Learning

Insights in AI applied to Credit Scoring

Having access to liquidity has been a major issue for humankind for financing both personal life aspects (e.g., housing, cars, college) and for business initiatives (e.g., starting, growing, and expanding a business). The amount of uncertainty that both faces of this exchange – i.e., the creditor and the prospective debtor – face are paramount. On […]

Written by on Jul 27, 2021

ML System Design: Federated Learning

NILG.AI, together with Neu.ro decided to try a format similar to a Reading Club, where the topic is not a specific paper but an entire research area. After a short discussion, we had a System Design part where the team described a specific use case to apply the new approach. Ideally, the discussion would stick […]

Written by on Jul 14, 2021

Automated Valuation Models for Real Estate

When we decide to buy or rent a real estate (apartment, room, house, etc), one of the most important search criteria is the price. Its value depends mostly on characteristics, such as location, year of construction, number of rooms, area, central heating, etc. However, two properties with the same characteristics, for example, can be sold […]

Written by on Jun 14, 2021

An Introduction to Multiple Instance Learning

Multiple Instance Learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag, opposedly to the instances themselves. This allows to leverage weakly labeled data, which is present in many business problems as labeling data is often costly: Medical […]

Written by on May 18, 2021

Address validation for optimizing delivery success rate

In recent years, the e-commerce industry has been increasing significantly in several sectors like food, retailing, and electronics. In the latter part of 2020, this increase has been even higher with growth 3 times greater than the previous years, mostly due to the pandemic context we live in, which boosted the number of online shopping. […]

Written by on Mar 17, 2021

Embedding Domain Knowledge

In the good old days, working as a Machine Learning Engineer meant working 95% of the time on feature engineering and 5% on training models with the extracted features. This was a manually intensive and time-consuming process, that usually led to inflexible proofs of concept that could hardly be adapted to new settings. Fortunately, Deep […]

Written by on Feb 17, 2021

An Overview of Churn Prediction

Churn prediction – tandem with engagement – is probably the most wanted use case we get from Marketing departments across industries. For those of you that do not know what churn is, basically it’s associated with customers that will leave your company/services. So, it shouldn’t be a surprise that companies put a lot of effort […]

Written by on Jan 20, 2021

Reducing Unemployment using AI

With COVID-19, many were affected by the economic crisis and lost their jobs. In Portugal alone, between February and September, there was a 30% increase in unemployment! AI can be a powerful tool in allocating scarce resources in a more efficient way. Inspired by DSSG Fellowship’s Project in Partnership with IEFP (Instituto de Emprego e […]

Written by on Jan 18, 2021

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