How is logistic regression different from decision trees?

480    Asked by AishwaryaJhadav in Data Science , Asked on Nov 5, 2019
Answered by Aishwarya Jhadav

Logistic regression and decision tree are both classification based algorithms but they are very different from each other

  1. Logistic regression looks like an equation between independent variables with respect to dependent variable but decision tree works on simple sentences which produces an outcome based on the decision statement.
  2. Logistic regression is a parametric model that has parameter to predict the dependent variable but decision tree is a non-parametric model in which no parameter exists.
  3. Assumptions can be made target variables with binomial or Bernoulli distribution but no assumptions can be made on the decision tree algorithm
  4. Shape of the model is predefined as logistic curve but it is not predefined in decision tree algorithm
  5. Logistic algorithms provide good results when independent variables are continuous in nature but decision trees produce results when the variables are categorical in nature.
  6. Logistic regression finds it difficult to interact with non linear variables they do not affect the performance in case of decision tree algorithm.
  7. Outliers and missing values reduce the performance of logistic regression but they do not affect the performance of decision tree algorithm.



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