Machine Learning Resources

What are some pros and cons of logistic regression?


  • Through a simple transformation, much of the interpretability and intuition of linear regression is preserved
  • Efficient to implement and requires little tuning of parameters
  • Predicted probabilities are usually well calibrated, which is often not the case in some machine learning algorithms. This means that, for example, it is accurate to translate a predicted probability of 0.10 to a 10% probability of success for that observation. 


  • Does not automatically learn a non-linear decision boundary, and it is necessary to explicitly define any non-linear relationships by transforming the original data and adding additional terms to the model
  • Does not perform as well as some machine learning algorithms in modeling more complex, high-dimensional relationships

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