Es una realidad que la IA es una herramienta muy valiosa para combatir el fraude, ya que su capacidad para analizar datos y detectar patrones sospechosos supera en muchos casos la capacidad humana. Sin embargo, el uso de modelos de Machine Learning en nuestros sistemas de detección de fraude puede
It’s hard to get through a day in analytics now without hearing the words interpretability and explainability. These terms have become important in a world where machine learning and artificial intelligence (AI) models are becoming more ubiquitous. However, what do the two terms mean—and more importantly, why do they matter?
SAS' Brian Gaines provides a primer on GAMs.
Luego de otro largo lapso, termino publicando el siguiente artículo de la serie ¡Explícate!. En este veremos cómo la teoría de juegos nos da una mano para interpretar mejor nuestros modelos de machine learning, utilizando las ideas del premio nobel de economía Lloyd Shapley. Entenderemos los conceptos detrás de
In the preceding two posts, we looked at issues around interpretability of modern black-box machine-learning models and introduced SAS® Model Studio within SAS® Visual Data Mining and Machine Learning. Now we turn our attention to programmatic interpretability.
In the second of a three-part series of posts, SAS' Funda Gunes and her colleague Ricky Tharrington summarize model-agnostic model interpretability in SAS Viya.
In the first of a three-part series of posts, SAS' Funda Gunes and her colleague Ricky Tharrington summarize model-agnostic model interpretability in SAS Viya.
We have updated our software for improved interpretability since this post was written. For the latest on this topic, read our new series on model-agnostic interpretability. While some machine learning models – like decision trees – are transparent, the majority of models used today – like deep neural networks, random forests, gradient boosting
We have updated our software for improved interpretability since this post was written. For the latest on this topic, read our new series on model-agnostic interpretability. Assessing a model`s accuracy usually is not enough for a data scientist who wants to know more about how a model is working. Often
We have updated our software for improved interpretability since this post was written. For the latest on this topic, read our new series on model-agnostic interpretability. Don`t jump into modelling. First, understand and explore your data! This is common advice for many data scientists. If your data set is messy,
We have updated our software for improved interpretability since this post was written. For the latest on this topic, read our new series on model-agnostic interpretability. As machine learning takes its place in many recent advances in science and technology, the interpretability of machine learning models grows in importance. We
