Black box machine learning models are currently being used for high-stakes decision
making throughout society, causing problems in healthcare, criminal justice and other …

Decision trees have the particularity of being machine learning models that are visually easy
to interpret and understand. Therefore, they are primarily suited for sensitive domains like …

State-of-the-art decision tree methods apply heuristics recursively to create each split in
isolation, which may not capture well the underlying characteristics of the dataset. The …

Pattern recognition has long been studied in relation to many different (and mainly
unrelated) applications, such as remote sensing, computer vision, space research, and …

A reported weakness of C4. 5 in domains with continuous attributes is addressed by
modifying the formation and evaluation of tests on continuous attributes. An MDL-inspired …

Twenty-two decision tree, nine statistical, and two neural network algorithms are compared
on thirty-two datasets in terms of classification accuracy, training time, and (in the case of …

We present an algorithm for building probabilistic rule lists that is two orders of magnitude
faster than previous work. Rule list algorithms are competitors for decision tree algorithms …

A great many tools have been developed for supervised classification, ranging from early
methods such as linear discriminant analysis through to modern developments such as …

Induced decision trees are an extensively-researched solution to classi cation tasks. For
many practical tasks, the trees produced by tree-generation algorithms are not …

There is growing interest in scaling up the widely-used decision-tree learning algorithms to
very large data sets. Although numerous diverse techniques have been proposed, a fast tree …