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Could you explain the dissimilarity between the brief descriptions of Logistic Regression and SVM?

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Anonymous

3.5Strong
Both the approaches are linear classification approaches. First logistic regression utilizes a sigmoid function to plot the data onto the sigmoid curve and maximizes likelihood of the input. SVM, on the other hand, utilizes support vectors or vectors which help create a margin which classifies the two classes very well. This, along with kernel trick, the trick to add non-linearity to modeling using a polynomial or rbf kernel without converting the data into the space makes it more robust.t Both the models are binary classifiers and relatively fast to train
  • Could you explain the dissimilarity between the brief descriptions of Logistic Regression and SVM?
  • How do the summaries of Logistic Regression and SVM vary from each other?
  • What sets apart the abstracts of Logistic Regression and SVM?
  • Please distinguish the synopses of Logistic Regression and SVM.
  • In what ways do the summaries of Logistic Regression and SVM differ?
  • What is the discrepancy between the overviews of Logistic Regression and SVM?
  • Can you elaborate on the contrasts between the summaries of Logistic Regression and SVM?
  • How do the summarizations of Logistic Regression and SVM deviate from each other?
  • What distinguishes the truncated versions of Logistic Regression and SVM?
  • Please explain the contrast between the abstracts of Logistic Regression and SVM.
  • What's the difference between the summaries of a Logistic Regression and SVM?
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Interview question asked to Machine Learning Engineers interviewing at SAP Concur, Binance, Amazon and others: Could you explain the dissimilarity between the brief descriptions of Logistic Regression and SVM?.