GRNN with Small Samples
After a bank launches a new product or acquires a new portfolio, the risk modeling team would often be faced with a challenge of how to estimate the corresponding performance,...continue reading.
Category: Rstat
satRday Cardiff 2019 recap
SatRday events have become a more and more popular format of R conference series. They are based on such values as inclusivity, affordability and are made for and by real...continue reading.
Teach Yourself R in 24 Hours
With the open source R programming language and its immense library of packages, you can perform virtually any data analysis task. Now, in just 24 lessons of one hour or...continue reading.
shortened iterations [code golf]
A codegolf lazy morning exercise towards finding the sequence of integers that starts with an arbitrary value n and gets updated by blocks of four as until the last term...continue reading.
The Chaos Game: an experiment about fractals, recursivity and creative coding
Mathematics, rightly viewed, possesses not only truth, but supreme beauty (Bertrand Russell) You have a pentagon defined by its five vertex. Now, follow these steps: Step 0: take a point...continue reading.
How to Grow Your Own Data Scientists – a practical guide for the data-driven C-Suite
Data today is the fuel driving the modern business world. It therefore stands to reason that the ability to read and speak data should be a fairly mainstream skill. Except it isn’t -...continue reading.
Data Science – A New Hope for Good
Good triumphing over evil in the end is the stuff of every good fairy tale or Hollywood storyline, but in real life, as we all know, it’s usually the tales...continue reading.
GRNN vs. GAM
In practice, GRNN is very similar to GAM (Generalized Additive Models) in the sense that they both shared the flexibility of approximating non-linear functions. In the example below, both GRNN...continue reading.
Super Solutions for Shiny Architecture #5 of 5: Automated Tests
TL;DR Describes the best practices for setting automated test architecture for Shiny apps. Automate and test early and often with unit tests, user interface tests, and performance tests. Best Practices...continue reading.
Introducing interns to data science: The Cost of Edinburgh
This summer Mango took on three summer interns, Chris, Ellena and Lizzi, all maths students at different stages of their university careers. To provide insight into what it’s like to...continue reading.
Permutation Feature Importance (PFI) of GRNN
In the post https://statcompute.wordpress.com/2019/10/13/assess-variable-importance-in-grnn, it was shown how to assess the variable importance of a GRNN by the decrease in GoF statistics, e.g. AUC, after averaging or dropping the variable...continue reading.
Partial Dependence Plot (PDP) of GRNN
The function grnn.margin() (https://github.com/statcompute/yager/blob/master/code/grnn.margin.R) was my first attempt to explore the relationship between each predictor and the response in a General Regression Neural Network, which usually is considered the Black-Box...continue reading.
French Habilitation: Bayesian statistical learning and applications
Long time no see, Statisfaction! I’m glad to write about my habilitation entitled Bayesian statistical learning and applications I defended yesterday at Inria Grenoble. This Habilitation à Diriger des Recherches...continue reading.
Merge MLP And CNN in Keras
In the post (https://statcompute.wordpress.com/2017/01/08/an-example-of-merge-layer-in-keras), it was shown how to build a merge-layer DNN by using the Keras Sequential model. In the example below, I tried to scratch a merge-layer DNN...continue reading.
Assess Variable Importance In GRNN
Technically speaking, there is no need to evaluate the variable importance and to perform the variable selection in the training of a GRNN. It’s also been a consensus that the...continue reading.
Hyper-Parameter Optimization of General Regression Neural Networks
A major advantage of General Regression Neural Networks (GRNN) over other types of neural networks is that there is only a single hyper-parameter, namely the sigma. In the previous post...continue reading.
Super Solutions for Shiny Architecture 3/5: Softcoding Constants in the App
TL;DR Two methods for keeping your Shiny app organized while avoiding hardcoding. Softcoding Constants in a Shiny App They can be found everywhere. Text on buttons, urls to be linked,...continue reading.
JRSS: Series B read paper and comparison with other unbiased estimators
Hi all, The paper “unbiased Markov chain Monte Carlo with couplings” co-written with John O’Leary and Yves Atchadé has been accepted as a read paper in JRSS: Series B, to...continue reading.
Colonizing Franky
Y otra vez me arranco despacito, al sentir que nada necesito (Locura transitoria, Extremoduro) One of my favorite sites in the Internet is algorithmic botany . It’s always a source...continue reading.
BayesBag, and how to approximate it
Hi all, This post describes how unbiased MCMC can help in approximating expectations with respect to “BayesBag”, an alternative to standard posterior distributions mentioned in Peter Bühlmann‘s discussion of Big...continue reading.
