Remove 2001 Remove Forecasting Remove Metrics Remove Visualization
article thumbnail

Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics. ” “Data science” was first used as an independent discipline in 2001.

article thumbnail

Reclaiming the stories that algorithms tell

O'Reilly on Data

Each of the classroom’s library books has a color coded sticker on its spine reflecting its Lexile score—a visual announcement of its official complexity level, and thus of which students might be officially ready to read it. This whole scoring system also changes the story about who librarians and teachers are.

Risk 355
article thumbnail

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

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.