Machine learning (ML) models are only as good as the data fed into them. In tabular problems, the data is a collection of rows (samples) and columns (features). So, you could say that tabular ML models are only as good as the features fed into them. But how do you manage features? Can you share […]

Note: this is a community blog post by Shamil Dilshan Prematunga. It was first published on Medium. When we step into the AI application world it is not one easy step. It has a series of tasks that are combined. To convert an idea to the workable stage we must fulfill the requirements in each […]

If you can’t explain it to a six-year-old, you don’t understand it yourself. – Albert Einstein One fear caused by machine learning (ML) models is that they are blackboxes that cannot be explained. Some are so complex that no one, not even domain experts, can understand why they make certain decisions. This is of particular […]

The vast majority of the equities market trades electronically while the bond market is still in its infancy by comparison, but MarketAxess is seeking to change that. Recently, we hosted a virtual event with the MarketAxess team where they explained how they were solving challenges in the world’s largest bond marketplace while leveraging AI and […]

There’s a new major release of H2O, and it’s packed with new features and fixes! Among the big new features in this release are Distributed Uplift Random Forest, an algorithm typically used in marketing and medicine to model uplift, and Infogram, a new research direction in machine learning that focuses on interpretability and fairness in Admissible […]

Kaggle, the largest global community of data scientists, conducted the 5th annual industry-wide survey that presented a truly comprehensive view of the state of data science and machine learning. A total of 25,973 responses were collected from participants from over 60 countries. Kaggle also launched the Data Science Survey Challenge in which the goal was […]

For years machine learning (ML) researchers have focused on building outstanding models and figuring out how to squeeze every last drop of performance from them. But many have realized that creating top-performing models doesn’t necessarily equate to having them deliver business value. Often the best models can be very complex and costly to run in […]

In the first article in this series, we broke down the preprocessing and feature engineering techniques needed to build high-performing time series models. But we didn’t discuss the models themselves. In this article, we will dig into this. As a quick refresher, time series data has time on the x-axis and the value you are […]