This paper presents a novel algorithm called Logistic Model Trees (LMT) that combines logistic regression models with tree induction methods for classification tasks. Unlike traditional model trees, which use linear regression at the leaves, LMT uses logistic regression to model class membership probabilities. The algorithm adaptively constructs logistic regression models at the leaves by refining models trained at higher levels in the tree, using a stagewise fitting process. This approach helps in selecting relevant attributes and refining models to capture local patterns without overfitting. The performance of LMT is evaluated on 36 UCI datasets compared to several state-of-the-art learning schemes, including C4.5, CART, logistic regression, and other tree-based classifiers. The results show that LMT produces more accurate and compact classifiers, with competitive performance against boosted decision trees while maintaining interpretability. The paper also discusses related tree-based learning schemes and provides a detailed description of the LMT algorithm, including its implementation and experimental results.This paper presents a novel algorithm called Logistic Model Trees (LMT) that combines logistic regression models with tree induction methods for classification tasks. Unlike traditional model trees, which use linear regression at the leaves, LMT uses logistic regression to model class membership probabilities. The algorithm adaptively constructs logistic regression models at the leaves by refining models trained at higher levels in the tree, using a stagewise fitting process. This approach helps in selecting relevant attributes and refining models to capture local patterns without overfitting. The performance of LMT is evaluated on 36 UCI datasets compared to several state-of-the-art learning schemes, including C4.5, CART, logistic regression, and other tree-based classifiers. The results show that LMT produces more accurate and compact classifiers, with competitive performance against boosted decision trees while maintaining interpretability. The paper also discusses related tree-based learning schemes and provides a detailed description of the LMT algorithm, including its implementation and experimental results.