O'Reilly on Data

DECEMBER 12, 2019

Model debugging is an emergent discipline focused on finding and fixing problems in ML systems. In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Interpretable ML models and explainable ML. If so, have fun debugging! [1]

Machine Learning 281

Machine Learning Modeling Testing Risk Management 281

Smart Data Collective

SEPTEMBER 17, 2020

Financial institutions such as banks have to adhere to such a practice, especially when laying the foundation for back-test trading strategies. A 2013 survey conducted by the IBM’s Institute of Business Value and the University of Oxford showed that 71% of the financial service firms had already adopted analytics and big data.

Big Data 145

Big Data Finance Risk Management Risk 145

Domino Data Lab

APRIL 21, 2021

The dataset contains transactions made by European credit card holders in September 2013, and has been anonymized – Features V1, V2, …, V28 are results from applying PCA on the raw data. This is to prevent any information leakage into our test set. 2f%% of the test set." 2f%% of the test set."

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

AUGUST 22, 2019

When running word2vec, you can choose between two underlying model architectures— skip-gram (SG) or continuous bag of words (CBOW; pronounced see-bo)— either of which will typically produce roughly comparable results despite maximizing probabilities from “opposite” perspectives. Relative to extrinsic evaluations, intrinsic tests are quick.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Jet Global

MAY 1, 2023

Companies like Tableau (which raised over $250 million when it had its IPO in 2013) demonstrated an unmet need in the market. Later on, you’ll appreciate being able to test ideas and leverage best practices as your needs evolve. Users’ varied needs require a shift in traditional BI thinking. Their dashboards were visually stunning.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52