October 2019

Kannada MNIST Prediction Classification using H2O AutoML in R

October 2, 2019 | 0 Comments

Kannada MNIST dataset is another MNIST-type Digits dataset for Kannada (Indian) Language. All details of the dataset curation has been captured in the paper titled: “Kannada-MNIST: A new handwritten digits dataset for the Kannada language.” by Vinay Uday Prabhu. The github repo of the author can be found here. The ...
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Counting NAs by column in R

October 1, 2019 | 0 Comments

Are you starting your data exploration? Do you want to have an easy overview of your variable NA percentage? We create a function to benchmark different ways of achieving it: library(microbenchmark) library(tidyverse) benchmark_count_na_by_column % summary(), # Numeric output colSums(is.na(dataset)), sapply(dataset, function(x) ... [Read more...]

Multiple Hypothesis Testing in R

October 1, 2019 | 0 Comments

In the first article of this series, we looked at understanding type I and type II errors in the context of an A/B test, and highlighted the issue of “peeking”. In the second, we illustrated a way to calculate always-valid p-values that were immune to peeking. We will now ... [Read more...]

Goodbye, Disqus! Hello, Utterances!

October 1, 2019 | 0 Comments

Removing Disqus from my blogdown blog had been on my mind for a while, ever since I saw Bob Rudis’ tweet enjoining Noam Ross to not use it for his brand-new website. The same Twitter thread introduced me to Utterances, a “lightweight comments widget built on GitHub issues”, which I ... [Read more...]

How to use math symbols with ggdag

October 1, 2019 | 0 Comments

The wonderful package ggdag can easily make DAG like this: However, what we really want to include in publications is something like this: The second one can include subscript and superscript, among many others. After some tweaking, I found a solution, not perfect but usable for now.-----------------------------------------------------------------library(dagitty)library(...
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How to use math symbols with ggdag

October 1, 2019 | 0 Comments

The wonderful package ggdag can easily make DAG like this: However, what we really want to include in publications is something like this: The second one can include subscript and superscript, among many others. After some tweaking, I found a solution, not perfect but usable for now.-----------------------------------------------------------------library(dagitty)library(...
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Part I: Operationalizing R models with Dash Enterprise and Microsoft Azure

October 1, 2019 | 0 Comments

While R offers excellent support for machine learning, the process of operationalizing ML models as interactive web applications offers a promising tool for collaboration and data visualization. A well-designed web application can help close the gap between analysts and project stakeholders by providing data scientists with a platform to communicate ...
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New vtreat Documentation (Starting with Multinomial Classification)

October 1, 2019 | 0 Comments

Nina Zumel finished some great new documentation showing how to use Python vtreat to prepare data for multinomial classification mode. And I have finally finished porting the documentation to R vtreat. So we now have good introductions on how to use vtreat to prepare data for the common tasks of: ... [Read more...]

Forecast Stability Guidance for Model Selection

October 1, 2019 | 0 Comments

Forecast Stability Guidance for Model Selection /*! jQuery v1.11.3 | (c) 2005, 2015 jQuery Foundation, Inc. | jquery.org/license */ !function(a,b){"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(... [Read more...]

Super Solutions for Shiny Architecture 2/5: Javascript Is Your Friend

October 1, 2019 | 0 Comments

TL;DR Three methods for using javascript code in Shiny applications to build faster apps, avoid unnecessary re-rendering, and add components beyond Shiny’s limits. Part 2 of a five part series on super solutions for Shiny architecture.  Why Javascript + Shiny?  Many Shiny creators had a data science background, and not ...
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Notes from a panel II: Value of successful BBSRC grants

October 1, 2019 | 0 Comments

This post follows on from the last post on BBSRC Responsive Mode funding. Another frequent question from applicants is: “How much can I ask for?” One answer is: the same amount as successful grants. This information is freely available and can be downloaded from the UKRI website. All awarded grants ...
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Fast adaptive spectral clustering in R (brain cancer RNA-seq)

October 1, 2019 | 0 Comments

Spectral clustering refers to a family of algorithms that cluster eigenvectors derived from the matrix that represents the input data’s graph. An important step in this method is running the kernel function that is applied on the input data to generate a NXN similarity matrix or graph (where N ...
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