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

IBM Big Data Hub

JULY 6, 2023

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. appeared first on IBM Blog.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M) Figure 3 shows visual explanation of how SMOTE generates synthetic observations in this case.

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

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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