Deep Learning, Metrics, Predictive Modeling and Risk Management

Why you should care about debugging machine learning models

O'Reilly on Data

DECEMBER 12, 2019

That’s where model debugging comes in. 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. Sensitivity analysis.

Machine Learning

Machine Learning Modeling Testing Risk Management

Leveraging user-generated social media content with text-mining examples

IBM Big Data Hub

AUGUST 28, 2023

Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs. If necessary, make adjustments to the preprocessing, representation and/or modeling steps to improve the results.

Data mining

Data mining Machine Learning Deep Learning Marketing

Data Leaders Brief

Why you should care about debugging machine learning models

Leveraging user-generated social media content with text-mining examples

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