Data Collection, Events, Forecasting and Knowledge Discovery

Fundamentals of Data Mining

Data Science 101

OCTOBER 31, 2019

This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). Data Collection. Anomaly Detection.

Data mining

Data mining KDD Data Science Forecasting

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large. A rule-learning program in high energy physics event classification. Smote: Synthetic minority over-sampling technique. 16(1), 321–357.

Machine Learning

Machine Learning Metrics Data mining Knowledge Discovery

Data Leaders Brief

Fundamentals of Data Mining

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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