Tree Induction vs. Logistic Regression: A Learning-Curve Analysis

  title={Tree Induction vs. Logistic Regression: A Learning-Curve Analysis},
  author={Claudia Perlich and Foster J. Provost and Jeffrey S. Simonoff},
  journal={Journal of Machine Learning Research},
Tree induction and logistic regression are two standard, off-the-shelf methods for building models for classification. We present a large-scale experimental comparison of logistic regression and tree induction, assessing classification accuracy and the quality of rankings based on class-membership probabilities. We use a learning-curve analysis to examine the relationship of these measures to the size of the training set. The results of the study show several remarkable things. (I) Contrary to… CONTINUE READING
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