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
Deep learning has taken off because organizations of all sizes are capturing a greater variety of data and can mine bigger data, including unstructured data. It’s not just large companies like Amazon, SAS and Google that have access to big data. It’s everywhere. Deep learning needs big data, and now
In machine learning, a feature is another word for an attribute or input, or an independent variable. What is feature engineering? Feature engineering is a process of preparing inputs for machine learning models. The goal of feature engineering is to to improve classification accuracy by considering the limitations of the
Several weeks ago, I wrote about practical advice from a Chief Data Scientist in my blog “From Aristotle to Pi: Practical advice from a chief data scientist.” Now I want to offer my advice as a newbie trying to navigate through machine learning concepts and how to code them. Over
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
This is the sixth post in my series of machine learning best practices. If you've come across the series for the first time, you can go back to the beginning or read the whole series. Aristotle was likely one of the first data scientists who studied empiricism by learning through
I started my training in machine learning at the University of Tennessee in the late 1980s. Of course, we didn’t call it machine learning then, and we didn’t call ourselves data scientists yet either. We used terms like statistics, analytics, data mining and data modeling. Regardless of what you call
Who says machine learning can't be fun? A crew of us from SAS went to San Francisco for the recent KDD conference, which bills itself as "a premier interdisciplinary conference, [which]brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data." We brought
Machine learning applications for NBA coaches and players might seem like an odd choice for me to write about. Let us get something out of the way: I don’t know much about basketball. Or baseball. Or even soccer, much to the chagrin of my friends back home in Europe. However,
Optimization for machine learning is essential to ensure that data mining models can learn from training data in order to generalize to future test data. Data mining models can have millions of parameters that depend on the training data and, in general, have no analytic definition. In such cases, effective models
