Over the past decades, classification models have proven to be essential machine learning
tools given their potential and applicability in various domains. In these years, the north of …

With the growing adoption of machine learning techniques, there is a surge of research
interest towards making machine learning systems more transparent and interpretable …

As an ensemble model that consists of many independent decision trees, random forests
generate predictions by feeding the input to internal trees and summarizing their outputs …

Research in machine learning has become very popular in recent years, with many types of
models proposed to comprehend and predict patterns and trends in data originating from …

Interpretation and diagnosis of machine learning models have gained renewed interest in
recent years with breakthroughs in new approaches. We present Manifold, a framework that …

Interpretable machine learning has become a very active area of research due to the rising
popularity of machine learning algorithms and their inherently challenging interpretability …

We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a stable rule
learning algorithm, which takes the form of a short and simple list of rules. State-of-the-art …

We propose a novel methodology, forest floor, to visualize and interpret random forest (RF)
models. RF is a popular and useful tool for non-linear multi-variate classification and …

Classifiers are among the most widely used supervised machine learning algorithms. Many
classification models exist, and choosing the right one for a given task is difficult. During …

Rule-based models, eg, decision trees, are widely used in scenarios demanding high model
interpretability for their transparent inner structures and good model expressivity. However …