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What are some pros and cons of Discriminant Analysis?

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  • Well-designed for multiclass problems
  • Closed form solution, meaning computationally efficient
  • Outputs class probabilities for each label
  • Does not require much hyperparameter tuning


  • Assumes multivariate normality among features, though it is relatively robust to this assumption
  • Not as well suited for mixed data types as decision tree based methods

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