2001, 2008, Forecasting and Metrics

Reclaiming the stories that algorithms tell

O'Reilly on Data

MAY 27, 2020

In 2001, just as the Lexile system was rolling out state-wide, a professor of education named Stephen Krashen took to the pages of the California School Library Journal to raise an alarm. His system was needed because “beginning teachers and librarians” were less expert at “forecasting comprehension rates” than the algorithm was.

Risk

Risk Testing Reporting Measurement

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

Working with highly imbalanced data can be problematic in several aspects: Distorted performance metrics — In a highly imbalanced dataset, say a binary dataset with a class ratio of 98:2, an algorithm that always predicts the majority class and completely ignores the minority class will still be 98% correct. return synthetic. References.

Machine Learning

Machine Learning Metrics Data mining Knowledge Discovery

Data Leaders Brief

Reclaiming the stories that algorithms tell

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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