Predictive Modeling: Techniques, Uses, and Key Takeaways

Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.
Visual Studio Magazine

Gaussian Process Regression from Scratch Using C#

GPR works well with small datasets and generates a metric of confidence of a predicted result, but it's moderately complex and the results are not easily interpretable, says Dr. James McCaffrey of ...
Visual Studio Magazine

Neural Regression Using PyTorch: Model Accuracy

Dr. James McCaffrey of Microsoft Research explains how to evaluate, save and use a trained regression model, used to predict a single numeric value such as the annual revenue of a new restaurant based ...
Semiconductor Engineering

An Introduction To Semiconductor Process Modeling

Semiconductor process engineers would love to develop successful process recipes without the guesswork of repeated wafer testing. Unfortunately, developing a successful process can’t be done without ...
JSTOR Daily

Bayesian log-Gaussian Cox process regression: applications to meta-analysis of neuroimaging working memory studies

Pantelis Samartsidis, Claudia R. Eickhoff, Simon B. Eickhoff, Tor D. Wager, Lisa Feldman Barrett, Shir Atzil, Timothy D. Johnson, Thomas E. Nichols Journal of the ...
Kellogg School of Management

Interpreting a Regression Analysis

Beside the model, the other input into a regression analysis is some relevant sample data, consisting of the observed values of the dependent and explanatory variables for a sample of members of the ...
Semiconductor Engineering

Pattern Dependence Process Modeling

First order process modeling can help tremendously with process setup and integration challenges that occur in a semiconductor fabrication flow, by visualizing process variation problems “virtually” ...