PAC: Probably Approximately Correct

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#Data

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References:
  1. Shalev-Shwartz, S., & Ben-David, S. (2013). Understanding machine learning: From theory to algorithms. Understanding Machine Learning: From Theory to Algorithms.
Similar Articles:
Data Types
Gini Impurity
The Gini impurity is a measurement of the impurity of a set.
Information Gain
The information is a measurement of the entropy of the dataset.
Dealing with Missing Data in Machine Learning
During feature engineering, we have to deal with missing values.

L Ma (2020). 'PAC: Probably Approximately Correct', Datumorphism, 01 April. Available at: https://datumorphism.leima.is/cards/machine-learning/learning-theories/pac/.