O'Reilly on Data

MARCH 20, 2019

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. Applying data integrity constraints on live, incoming data streams could have the same benefits.

Modeling 227

Modeling Machine Learning Predictive Modeling Consulting 227

O'Reilly on Data

DECEMBER 12, 2019

Residual analysis is another well-known family of model debugging techniques. Residuals are a numeric measurement of model errors, essentially the difference between the model’s prediction and the known true outcome. Interpretable ML models and explainable ML. Residual analysis.

Machine Learning 198

Machine Learning Modeling Testing Risk Management 198

Cloudera

SEPTEMBER 7, 2023

This puts the onus on institutions to implement robust data encryption standards, process sensitive data locally, automate auditing, and negotiate clear ownership clauses in their service agreements. But these measures alone may not be sufficient to protect proprietary information. Train and upskill employees.

Insurance 83

Insurance Risk Data-driven Finance 83