413 search results for "pca"

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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Predicting food preferences with sparklyr (machine learning)

February 18, 2017
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Predicting food preferences with sparklyr (machine learning)

This week I want to show how to run machine learning applications on a Spark cluster. I am using the sparklyr package, which provides a handy interface to access Apache Spark functionalities via R. The question I want to address with machine learning ...

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Exploratory Multivariate Data Analysis with R- enroll now in the MOOC

February 17, 2017
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Exploratory Multivariate Data Analysis with R- enroll now in the MOOC

Exploratory multivariate data analysis is studied and has been taught in a “French-way” for a long time in France. You can enroll in a MOOC (completely free) on Exploratory Multivariate Data Analysis. The MOOC will start the 27th of February. This MOOC focuses on 4 essential and basic methods, those with the largest potential in terms of applications:

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Multilabel classification with neuralnet package

February 15, 2017
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Multilabel classification with neuralnet package

Some time ago I wrote an article on how to use a simple neural network in R with the neuralnet package to tackle a regression task. A few weeks ago, however, I was asked how to use the neuralnet package for making a multilabel classifier. I wrote a quick script as an example and thought I could write a short...

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MOOC on Exploratory Multivariate Data Analysis – enroll now

February 14, 2017
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MOOC on Exploratory Multivariate Data Analysis – enroll now

Exploratory multivariate data analysis is studied and teached in a French-way since a long time in France. You can enroll in a MOOC (completely free) on Exploratory Multivariate Data Analysis. The MOOC will start the 27th of February. This MOOC focuses on 4 essential and basic methods, those with the largest potential in terms of applications: principal component

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Factor Analysis Introduction with the Principal Component Method and R

February 9, 2017
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Factor Analysis Introduction with the Principal Component Method and R

Factor analysis is a controversial technique that represents the variables of a dataset as linearly related to random, unobservable variables called factors, denoted where . The factors are representative of ‘latent variables’ underlying the original variables. The existence of the factors is hypothetical as they cannot be measured or observed.... The post Factor Analysis Introduction with the Principal...

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Image Compression with Principal Component Analysis

January 26, 2017
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Image Compression with Principal Component Analysis

Image compression with principal component analysis is a frequently occurring application of the dimension reduction technique. Recall from a previous post that employed singular value decomposition to compress an image, that an image is a matrix of pixels represented by RGB color values. Thus, principal component analysis can be used... The post Image Compression with Principal Component Analysis...

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Principal Component Analysis in R

January 23, 2017
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Principal Component Analysis in R

Principal component analysis (PCA) is routinely employed on a wide range of problems. From the detection of outliers to predictive modeling, PCA has the ability of projecting the observations described by variables into few orthogonal components defined at where the data ‘stretch’ the most, rendering a simplified overview. PCA is particularly powerful in dealing with multicollinearity and variables that … Continue...

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Choosing Software to Publish your Data Science Portfolio

January 23, 2017
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Choosing Software to Publish your Data Science Portfolio

I’ve recently spoken to several people who Have decided to create a portfolio of their data science projects Are new to online publishing They frequently have... The post Choosing Software to Publish your Data Science Portfolio appeared first on AriLamstein.com.

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