DataRobot Blog

MAY 26, 2022

When the FRB’s guidance was first introduced in 2011, modelers often employed traditional regression -based models for their business needs. Examining these global feature importances enables the validator to understand the top data inputs and ensure that they fit with their domain expertise.

Risk 52

Risk Modeling Metrics Business Objectives 52

datapine

APRIL 15, 2020

It’s necessary to say that these processes are recurrent and require continuous evolution of reports, online data visualization , dashboards, and new functionalities to adapt current processes and develop new ones. The term “agile” was originally conceived in 2011 as a software development methodology.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

datapine

AUGUST 29, 2022

Whether you are a complete novice or a seasoned BI professional, you will find here some books on data analytics that will help you cultivate your understanding of this essential field. Before we delve deeper into the best books for data analytics, here are three big data insights to put their relevance and importance into perspective.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Domino Data Lab

MARCH 3, 2019

What metrics are used to evaluate success? There are essentially four types encountered: image/video, audio, text, and structured data. Note how model visualization is bubbling up to the top, which has implications for model interpretability, cyber threats, etc. Who builds their models?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49