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Advanced Analytics | Artificial Intelligence | Machine Learning

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Ilknur Kaynar KabulJune 12, 2018 0

Interpret model predictions with partial dependence and individual conditional expectation plots

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

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Advanced Analytics | Artificial Intelligence | Machine Learning

Yue QiJune 7, 2018 1

Recurrent neural networks: An essential tool for machine learning

Sequence models, especially recurrent neural network (RNN) and similar variants, have gained tremendous popularity over the last few years because of their unparalleled ability to handle unstructured sequential data. The reason these models are called “recurrent” is that they work with data that occurs in a sequence, such as text

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Advanced Analytics | Machine Learning

Susan KahlerMay 31, 2018 0

From Aristotle to Pi: Practical advice from a chief data scientist

What can you learn from a chief data scientist who's worked in analytics for for 25 years and has been involved in the development of many key SAS solutions, including SAS Enterprise Miner? As a veteran of the analytics industry, Wayne Thompson has witnessed the evolution of machine learning and

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Advanced Analytics | Artificial Intelligence | Machine Learning

Ilknur Kaynar KabulMarch 9, 2018 1

Understanding and interpreting your data set

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,

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Advanced Analytics | Artificial Intelligence | Machine Learning

Ilknur Kaynar KabulDecember 18, 2017 1

Interpretability is crucial for trusting AI and machine learning

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

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Advanced Analytics | Machine Learning

Xin HuntOctober 17, 2017 0

Online learning: Machine learning’s secret for big data

In the field of machine learning, online learning refers to the collection of machine learning methods that learn from a sequence of data provided over time. In online learning, models update continuously as each data point arrives. You often hear online learning described as analyzing “data in motion,” because it

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Advanced Analytics | Machine Learning

Xin HuntSeptember 25, 2017 1

Machine learning concepts: styles of machine learning

This is the first in a series of posts about machine learning concepts, where we'll cover everything from learning styles to new dimensions in machine learning research. What makes machine learning so successful? The answer lies in the core concept of machine learning: a machine can learn from examples and

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Advanced Analytics | Artificial Intelligence | Machine Learning

Wayne ThompsonSeptember 18, 2017 0

Is deep learning the latest fad?

In my 25 years at SAS, I‘ve noticed the continued use of important algorithms, such as logistic regression and decision trees, which I’m sure will continue to be steady staples for data scientists. After all, they’re easy-to-use, interpretable algorithms. However, they’re not always the most accurate and stable classifiers. To

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Advanced Analytics | Machine Learning

Wayne ThompsonSeptember 12, 2017 2

Machine learning best practices: Add features to training data

This is the final post in my series of machine learning best practices. If you missed the earlier posts, start at the beginning, or read the whole series by clicking on the image to the right. While post four in the series was about combining different types of models, this

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