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 …

Balancing accuracy gains with other objectives such as interpretability is a key challenge
when building decision trees. However, this process is difficult to automate because it …

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 …

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 …

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 …

Deep neural networks have been proven powerful at processing perceptual data, such as
images and audio. However for tabular data, tree-based models are more popular. A nice …

Predictive analytics embraces an extensive range of techniques including statistical
modeling, machine learning, and data mining and is applied in business intelligence, public …

With machine learning models being increasingly applied to various decision-making
scenarios, people have spent growing efforts to make machine learning models more …

Despite extensive research, it is still difficult to produce effective interactive layouts for large
graphs. Dense layout and occlusion make food webs, ontologies, and social networks …

Although deep neural networks have achieved state-of-the-art performance in several
artificial intelligence applications in the past decade, they are still hard to understand. In …