Although model-agnostic techniques exist for multi-target regression, specific techniques tailored to random forest models are not available.
As a result, the demand for a selection tool, a meta-explanation technique based on a high-quality evaluation metric, is apparent.
Transformers are widely used in natural language processing, where they consistently achieve state-of-the-art performance.
Random Forest falls short on this property, especially when a large number of tree predictors are used.
Dimensionality reduction (DR) is a popular method for preparing and analyzing high-dimensional data.
Artificial Intelligence (AI) has a tremendous impact on the unexpected growth of technology in almost every aspect.
LionForests is a random forest-specific interpretation technique, which provides rules as explanations.
The use of machine learning rapidly increases in high-risk scenarios where decisions are required, for example in healthcare or industrial monitoring equipment.
Lack of evaluation and selection criteria also makes it difficult for the end user to choose the most suitable technique.
Online hate speech is a recent problem in our society that is rising at a steady pace by leveraging the vulnerabilities of the corresponding regimes that characterise most social media platforms.
Ranked #1 on Hate Speech Detection on Ethos MultiLabel
Towards a future where machine learning systems will integrate into every aspect of people's lives, researching methods to interpret such systems is necessary, instead of focusing exclusively on enhancing their performance.
Technological breakthroughs on smart homes, self-driving cars, health care and robotic assistants, in addition to reinforced law regulations, have critically influenced academic research on explainable machine learning.