Nominalia, a region where the priests conduct Strange Rites
You have a nominal predictor variables with many values. That is to say, there are many categories and they do … Morecontinue reading.
Category: Machine learning
Coding Random Forests in 100 lines of code*
In our series of explaining method in 100 lines of code, we tackle random forest this time! We build it from scratch and explore it’s functions. Der Beitrag Coding Random...continue reading.
Google StreetView exercises in workshops
I’m now starting workshops on some topics with this small group exercise: You’re the CEO of a startup that’s going … Morecontinue reading.
Adding uncertainty estimates to Keras models with tfprobability
As of today, there is no mainstream road to obtaining uncertainty estimates from neural networks. All that can be said is that, normally, approaches tend to be Bayesian in spirit,...continue reading.
Jugando con las dimensiones: desde Clustering, PCA, t-SNE…. ¡hasta Carl Sagan!
Explora la intersección de conceptos como reducción de dimensiones, clustering, preparación de datos, PCA, HDBSCAN, k-NN, SOM, deep learning….y Carl Sagan!continue reading.
Using Reinforcement Learning to play Super Mario Bros on NES using TensorFlow
Could you #BeatTheAI? We let deep learning have a go at Super Mario’s first level and compared it to human players. Here we explain how we did it! Der Beitrag...continue reading.
Books for sale!
I’m clearing out some books. You can buy them! They are all good ones, it’s just that I don’t need … Morecontinue reading.
Hierarchical partial pooling, continued: Varying slopes models with TensorFlow Probability
This post builds on our recent introduction to multi-level modeling with tfprobability, the R wrapper to TensorFlow Probability. We show how to pool not just mean values (“intercepts”), but also...continue reading.
Review of “The Harvard Business Review Guide To Business Analytics Basics For Managers”
I picked this little book up at a railway station for two reasons: as a trainer, I wanted to find … Morecontinue reading.
Granular Weighted Binning by Generalized Boosted Model
In the post https://statcompute.wordpress.com/2019/04/27/more-general-weighted-binning, I’ve shown how to do the weighted binning with the function wqtl_bin() by the iterative partitioning. However, the outcome from wqtl_bin() sometimes can be too coarse....continue reading.
Test-driving analysis software
I recently started an exciting new project where I test-drive a wide range of software for data analysis. Mostly, these … Morecontinue reading.
Tadpoles on TensorFlow: Hierarchical partial pooling with tfprobability
This post is a first introduction to MCMC modeling with tfprobability, the R interface to TensorFlow Probability (TFP). Our example is a multi-level model describing tadpole mortality, which may be...continue reading.
Machine Learning explained for statistics folk
I’m running a one-day workshop called “From Statistics To Machine Learning” in central London on 28 October, for anyone who … Morecontinue reading.
DALEX for keras and parsnip
DALEX is a set of tools for explanation, exploration and debugging of predictive models. The nice thing about it is that it can be easily connected to different model factories....continue reading.
Summer 2019 workshops: clinical audit stats, and a data science primer for managers
I am doing two BayesCamp workshops in central London this summer: Statistical Analysis for Clinical Audit, 21 June [bookings] Data … Morecontinue reading.
Experimenting with autoregressive flows in TensorFlow Probability
Continuing from the recent introduction to bijectors in TensorFlow Probability (TFP), this post brings autoregressivity to the table. Using TFP through the new R package tfprobability, we look at the...continue reading.
Batch Deployment of WoE Transformations
After wrapping up the function batch_woe() today with the purpose to allow users to apply WoE transformations to many independent variables simultaneously, I have completed the development of major functions...continue reading.
Auto-Keras: Tuning-free deep learning from R
Sometimes in deep learning, architecture design and hyperparameter tuning pose substantial challenges. Using Auto-Keras, none of these is needed: We start a search procedure and extract the best-performing model. This...continue reading.
Batch Processing of Monotonic Binning
In my GitHub repository (https://github.com/statcompute/MonotonicBinning), multiple R functions have been developed to implement the monotonic binning by using either iterative discretization or isotonic regression. With these functions, we can run...continue reading.
Getting into the flow: Bijectors in TensorFlow Probability
Normalizing flows are one of the lesser known, yet fascinating and successful architectures in unsupervised deep learning. In this post we provide a basic introduction to flows using tfprobability, an...continue reading.
