Modeling

Total Tidy Tuning Techniques

rstudio::conf 2020

Total Tidy Tuning Techniques

February 12, 2020

Many models have structural parameters that cannot be directly estimated from the data. These tuning parameters can have a significant effect on model performance and require some mechanism for...

Stochastic Block Models with R: Statistically rigerous clusting with rigorous code

rstudio::conf 2020

Stochastic Block Models with R: Statistically rigerous clusting with rigorous code

January 31, 2020

Often a machine learning research project starts with brainstorming, continues to one-off scripts while an idea forms, and finally, a package is written to disseminate the product.

Neural Networks for Longitudinal Data Analysis

rstudio::conf 2020

Neural Networks for Longitudinal Data Analysis

January 31, 2020

Longitudinal data (or panel data) arise when observations are recorded on the same individuals at multiple points in time.

MLOps for R with Azure Machine Learning

rstudio::conf 2020

MLOps for R with Azure Machine Learning

January 31, 2020

Azure Machine Learning service (Azure ML) is Microsoft’s cloud-based machine learning platform that enables data scientists and their teams to carry out end-to-end machine learning workflows at scale.

Why TensorFlow eager execution matters

rstudio::conf 2019

Why TensorFlow eager execution matters

January 25, 2019

In current deep learning with Keras and TensorFlow, when you've mastered the basics and are ready to dive into more involved applications (such as generative networks, sequence-to-sequence or...

Visualizing uncertainty with hypothetical outcomes plots

rstudio::conf 2019

Visualizing uncertainty with hypothetical outcomes plots

January 25, 2019

Uncertainty is a key component of statistical inference. However, uncertainty is not easy to convey effectively in data visualizations. For example, viewers have a tendency to...

Solving the model representation problem with broom

rstudio::conf 2019

Solving the model representation problem with broom

January 25, 2019

The R objects used to represent model fits are notoriously inconsistent, making data analysis inconvenient and frustrating. The broom package resolves this issue by defining a consistent way to...

parsnip: A tidy model interface

rstudio::conf 2019

parsnip: A tidy model interface

January 24, 2019

parsnip is a new tidymodels package that generalizes model interfaces across packages. The idea is to have a single function interface for types of specific models (e.g. logistic regression) that...