2001, Blog, Knowledge Discovery and Modeling

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. In this blog post we talked about why working with imbalanced datasets is typically problematic, and covered the internals of SMOTE – a go-to technique for up-sampling minority classes. References.

Machine Learning

Machine Learning Metrics Data mining Knowledge Discovery

The Semantic Web: 20 Years And a Handful of Enterprise Knowledge Graphs Later

Ontotext

JULY 29, 2021

One of its pillars are ontologies that represent explicit formal conceptual models, used to describe semantically both unstructured content and databases. We rather see it as a new paradigm that is revolutionizing enterprise data integration and knowledge discovery. We can’t imagine looking at the Semantic Web as an artifact.

Enterprise

Enterprise Metadata Knowledge Discovery Management

Data Leaders Brief

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

The Semantic Web: 20 Years And a Handful of Enterprise Knowledge Graphs Later

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