411 search results for "pCA"

R and Singularity

March 29, 2017
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by Bryan Lewis R (https://www.r-project.org) is a premier system for statistical and scientific computing and data science. At its core, R is a very carefully curated high-level interface to low-level numerical libraries. True to this principle, R packages have greatly expanded the scope and number of these interfaces over the years, among them interfaces to

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R and Singularity

March 28, 2017
By
R and Singularity

R (https://www.r-project.org) is a premier system for statistical and scientific computing and data science. At its core, R is a very carefully curated high-level interface to low-level numerical libraries. True to this principle, R packages have greatly expanded the scope and number of these interfaces over the years, among them interfaces to a large number of distributed...

Read more »

R and Singularity

March 28, 2017
By
R and Singularity

R (https://www.r-project.org) is a premier system for statistical and scientific computing and data science. At its core, R is a very carefully curated high-level interface to low-level numerical libraries. True to this principle, R packages have greatly expanded the scope and number of these interfaces over the years, among them interfaces to a large number of distributed...

Read more »

Emojis Analysis in R

March 23, 2017
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Emojis Analysis in R

A while ago I developed and shared an emoji decoder because I was facing problems when retrieving data from Twitter and Instragram. In a nutshell, the issue is that R encodes emojis in a way that makes it a hassle identifying them. This is where t...

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New Course: Unsupervised Learning in R

March 15, 2017
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New Course: Unsupervised Learning in R

Hi there - today we're launching a new machine learning course on Unsupervised Learning in R by Hank Roark! Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. ...

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Missing values imputation with missMDA

March 7, 2017
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Missing values imputation with missMDA

“The best thing to do with missing values is not to have any” (Gertrude Mary Cox) Unfortunately, missing values are ubiquitous and occur for plenty of reasons. One solution is single imputation which consists in replacing missing entries with plausible values. It leads to a complete dataset that can be analyzed by any statistical methods. Based on dimensionality reduction

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The R Formula Method: The Bad Parts

March 1, 2017
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R’s model formula infrastructure was discussed in my previous post. Despite the elegance and convenience of the formula method, there are some aspects that are limiting. Limitations to Extensibility The model formula interface does have some limitations: It can be kludgy with many operations on many variables (e.g., log transforming 50 variables via a formula

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The R Formula Method: The Bad Parts

February 28, 2017
By
The R Formula Method: The Bad Parts

R’s model formula infrastructure was discussed in my previous post. Despite the elegance and convenience of the formula method, there are some aspects that are limiting. Limitations to Extensibility The model formula interface does have some limitations: It can be kludgy with many operations on many variables (e.g., log transforming 50 variables via a formula without using paste) The predvars...

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Building deep neural nets with h2o and rsparkling that predict arrhythmia of the heart

February 27, 2017
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Building deep neural nets with h2o and rsparkling that predict arrhythmia of the heart

Last week, I introduced how to run machine learning applications on Spark from within R, using the sparklyr package. This week, I am showing how to build feed-forward deep neural networks or multilayer perceptrons. The models in this example are built ...

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Factoextra R Package: Easy Multivariate Data Analyses and Elegant Visualization

February 19, 2017
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Factoextra R Package: Easy Multivariate Data Analyses and Elegant Visualization

factoextra is an R package making easy to extract and visualize the output of exploratory multivariate data analyses, including: Principal Component Analysis (PCA), which is used to summarize the information contained in a continuous (i.e, quantitative) multivariate data by reducing the dimensionality of...

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