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Machine Learning with Imbalanced Data

Machine Learning with Imbalanced Data

Machine Learning with Imbalanced Data, Learn multiple techniques to tackle data imbalance and improve the performance of your machine learning models.

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What you'll learn

  • Under-sampling methods at random
  • Under-sampling methods which focus on observations that are harder to classify
  • Under-sampling methods that ignore potentially noisy observations
  • Over-sampling methods to increase the number of minority observations
  • Ways of creating syntethic data to increase the examples of the minority class
  • SMOTE and its variants
  • Use ensemble methods with sampling techniques to improve model performance
  • The most suitable evaluation metrics to use with imbalanced datasets
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