Subconscious Musings
Advanced analytics - from Research Drive to the world
Continuing our series on model interpretability, this post explains two methods for plotting variables that can give insight into how a model is working. 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 data scientists
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
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
SAS® supports the creation of deep neural network models. Examples of these models include convolutional neural networks, recurrent neural networks, feedforward neural networks and autoencoder neural networks. Let’s examine in more detail how SAS creates deep learning models using SAS® Visual Data Mining and Machine Learning. Deep learning models with
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, building models will not help you to solve your problem. What will happen is “garbage in, garbage out.” In order to build a powerful machine learning
As machine learning takes its place in many recent advances in science and technology, the interpretability of machine learning models grows in importance. We are surrounded with applications powered by machine learning, and we’re personally affected by the decisions made by machines more and more every day. From the mundane