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A mobile visual clothing search system is presented whereby a smart phone user can either choose a social networking image or capture a new photo of a person wearing clothing of interest and search for similar clothing in a large cloud-based ecommerce database. The phone's GPS location is used to re-rank results by retail store location, to inform the user of local stores where similar clothing items can be tried on.
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When I saw this article I tought “Would it be possible to repreduce these results” and “Can I get higher AUC”? In this post I am going to use the data provide by the autors and will try to reproduce the result using R and caret packege for machine learning. First we have to load the data library(rfigshare) library(readxl) library(caret) library(pROC) trellis.par.set(caretTheme()) # download.file("https://ndownloader.figshare.com/files/7331495", "data/") S1Table.df <- read_excel("../../data/S1Table.xlsx") S1Table <- na.

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library(ggplot2)
df <- data.frame(x = rnorm(1000), y = rnorm(1000))
ggplot(df, aes(x, y)) + 
    geom_point() +
    theme_classic()

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This is something

summary(cars)
##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00
fit <- lm(dist ~ speed, data = cars)
plot(fit, which = 1)

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