The Advanced Analytics group at Seagate Technology has decided to share an internal project which helps accelerate development for classification problems. The interactive SeaClass tool is contained in an R based package built using R Shiny and other CRAN packages commonly used for binary classification. The package is free to use and develop further, but any analysis mistakes are the sole responsibility of the user. Checkout the demo video here.

The SeaClass R package provides tools for analyzing classification problems. In particular, specialized tools are available for addressing the problem of imbalanced data sets. The SeaClass application provides an easy to use interface which requires only minimal R programming knowledge to get started, and can be launched using the RStudio Addins menu. The application allows the user to explore numerous methods by simply clicking on the available options and interacting with the generated results. The user can choose to download the codes for any procedures they wish to explore further. SeaClass was designed to jump start the analysis process for both novice and advanced R users. See screenshots below for one demonstration.

The SeaClass application depends on numerous R packages. To install SeaClass and its dependencies run:

install.packages('devtools')
devtools::install_github('ChrisDienes/SeaClass')

Step 1. Begin by loading and preparing your data in R. Some general advice:

• Your data set must be saved as an R data frame object.
• The data set must contain a binary response variable (0/1, PASS/FAIL, A/B, etc.)
• All other variables must be predictor variables.
• Predictor variables can be numeric, categorical, or factors.
• Including too many predictors may slow down the application and weaken performance.
• Categorical predictors are often ignored when the number of levels exceeds 10 since they tend to have improper influences.
• Missing values are not allowed and will throw a flag. Please remove or impute NAs prior to starting the app.
• Keep the number of observations (rows) to a medium or small size.
• Data sets with many rows (>10,000) or many columns (>30) may slow down the app's interactive responses.

Step 2. After data preparation, start the application by either loading SeaClass from the RStudio Addins dropdown menu or by loading the SeaClass function from the command line. For example:

library(SeaClass)

### Make some fake data:
X <- matrix(rnorm(10000,0,1),ncol=10,nrow=1000)
X[1:100,1:2] <- X[1:100,1:2] + 3
Y <- c(rep(1,100), rep(0,900))
Fake_Data <- data.frame(Y = Y , X)

### Load the SeaClass rare failure data:
data("rareFailData")

### Start the interactive GUI:
SeaClass()

If the application fails to load, you may need to first specify your favorite browser path. For example:

options(browser = "C:/Program Files (x86)/Google/Chrome/Application/chrome.exe")

Step 3. The user has various options for configuring their analysis within the GUI. Once the analysis runs, the user can view the results, interact with the results (module dependent), save the underlying R script, or start over. Additional help is provided within the application. See above screenshots for one depiction of these steps.

Step 4. Besides the SeaClass function, several other functions are contained within the library. For example:

### List available functions:
ls("package:SeaClass")
### Note this is a sample data set:
# data(rareFailData)
### Note code_output is a support function for SeaClass, not for general use.

### View help:
?accuracy_threshold

### Run example from help file:
### General Use: ###
set.seed(123)
x <- c(rnorm(100,0,1),rnorm(100,2,1))
group <- c(rep(0,100),rep(2,100))
accuracy_threshold(x=x, group=group, pos_class=2)
accuracy_threshold(x=x, group=group, pos_class=0)
### Bagged Example ###
set.seed(123)
replicate_function = function(index){accuracy_threshold(x=x[index], group=group[index], pos_class=2)[[2]]}
sample_cuts <- replicate(100, {
  sample_index = sample.int(n=length(x),replace=TRUE)
  replicate_function(index=sample_index)
})
bagged_scores <- sapply(x, function(x) mean(x > sample_cuts))
unbagged_cut    <- accuracy_threshold(x=x, group=group, pos_class=2)[[2]]
unbagged_scores <- ifelse(x > unbagged_cut, 1, 0)
# Compare AUC:
PRROC::roc.curve(scores.class0 = bagged_scores,weights.class0 = ifelse(group==2,1,0))[[2]]
PRROC::roc.curve(scores.class0 = unbagged_scores,weights.class0 = ifelse(group==2,1,0))[[2]]
bagged_prediction <- ifelse(bagged_scores > 0.50, 2, 0)
unbagged_prediction <- ifelse(x > unbagged_cut, 2, 0)
# Compare Confusion Matrix:
table(bagged_prediction, group)
table(unbagged_prediction, group)