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In this article, you learn how to make Automated Dashboard for Classification Neural Network in R. First you need to install the `rmarkdown` package into your R library. Assuming that you installed the `rmarkdown`, next you create a new `rmarkdown` script in R.

After this you type the following code in order to create a dashboard with `rmarkdown`

and `flexdashboard`

:

--- title: "Dashboard Classification Neural Network in R" author: "Kristian Larsen" output: flexdashboard::flex_dashboard: orientation: rows vertical_layout: scroll --- ```{r setup, include=FALSE} # Data management packages library(tidyverse) library(neuralnet) library(GGally) library(flexdashboard) library(plotly) # Read dataset url <- 'http://archive.ics.uci.edu/ml/machine-learning-databases//haberman/haberman.data' Hab_Data % na.omit() %>% mutate(Survival = ifelse(Survival == 2, 0, 1), Survival = factor(Survival)) scale01 <- function(x){ (x - min(x)) / (max(x) - min(x)) } Hab_Data % mutate(Age = scale01(Age), Operation_Year = scale01(Operation_Year), Number_Pos_Nodes = scale01(Number_Pos_Nodes), Survival = as.numeric(Survival)-1) Hab_Data % mutate(Survival = as.integer(Survival) - 1, Survival = ifelse(Survival == 1, TRUE, FALSE)) ``` Row {data-width=350} ----------------------------------------------------------------------- ### Chart A - Scattorplot matrix ```{r} ggpairs(Hab_Data, title = "Scatterplot Matrix of the Features of the Haberman's Survival Data Set") ggplotly(p = ggplot2::last_plot()) ``` ### Chart B - 1st Classification ANN ```{r} set.seed(123) Hab_NN1 <- neuralnet(Survival ~ Age + Operation_Year + Number_Pos_Nodes, data = Hab_Data, linear.output = FALSE, err.fct = 'ce', likelihood = TRUE) plot(Hab_NN1, rep = 'best') ``` Row {data-width=650} ----------------------------------------------------------------------- ### Chart C - Classification Hyperparameters ```{r} Hab_NN1_Train_Error <- Hab_NN1$result.matrix[1,1] #paste("CE Error: ", round(Hab_NN1_Train_Error, 3)) Hab_NN1_AIC <- Hab_NN1$result.matrix[4,1] #paste("AIC: ", round(Hab_NN1_AIC,3)) Hab_NN2_BIC <- Hab_NN1$result.matrix[5,1] #paste("BIC: ", round(Hab_NN2_BIC, 3)) set.seed(123) # 2-Hidden Layers, Layer-1 2-neurons, Layer-2, 1-neuron Hab_NN2 <- neuralnet(Survival ~ Age + Operation_Year + Number_Pos_Nodes, data = Hab_Data, linear.output = FALSE, err.fct = 'ce', likelihood = TRUE, hidden = c(2,1)) # 2-Hidden Layers, Layer-1 2-neurons, Layer-2, 2-neurons set.seed(123) Hab_NN3 <- Hab_NN2 <- neuralnet(Survival ~ Age + Operation_Year + Number_Pos_Nodes, data = Hab_Data, linear.output = FALSE, err.fct = 'ce', likelihood = TRUE, hidden = c(2,2)) # 2-Hidden Layers, Layer-1 1-neuron, Layer-2, 2-neuron set.seed(123) Hab_NN4 <- Hab_NN2 <- neuralnet(Survival ~ Age + Operation_Year + Number_Pos_Nodes, data = Hab_Data, linear.output = FALSE, err.fct = 'ce', likelihood = TRUE, hidden = c(1,2)) # Bar plot of results Class_NN_ICs % ggplot(aes(Network, Value, fill = Metric)) + geom_col(position = 'dodge') + ggtitle("AIC, BIC, and Cross-Entropy Error of the Classification ANNs", "Note: ce Error displayed is 100 times its true value") ggplotly(p = ggplot2::last_plot()) ```

The result of the above coding are published with RPubs here.

### References

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