nonet_ensemble classification with nonet

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nonet_ensemble classification with nonet_plot

nonet provides ensemble capabilities for classification problems.

Below example shows the step by step implementation of nonet_ensemble and nonet_plot functions in the context of classification. We have used Bank Note authentication data set to predict the output class variable using random forest and neural network models. Predictions from random forest model and neural network model are being used as inputs to the nonet_ensemble in the list form.

Let’s start:

Load the required libraries

library(caret)

## Loading required package: lattice

## Loading required package: ggplot2

library(ggplot2)
library(nonet)

Load the banknote_authentication dataset and explore it.

dataframe <- data.frame(banknote_authentication)
head(dataframe)

##   variance skewness curtosis  entropy class
## 1  3.62160   8.6661  -2.8073 -0.44699     0
## 2  4.54590   8.1674  -2.4586 -1.46210     0
## 3  3.86600  -2.6383   1.9242  0.10645     0
## 4  3.45660   9.5228  -4.0112 -3.59440     0
## 5  0.32924  -4.4552   4.5718 -0.98880     0
## 6  4.36840   9.6718  -3.9606 -3.16250     0

We can see above that class variable has int datatype, we need to convert it into factor so that classification models can be trained on that.

Converting datatype of class variable into factor which have two classes Yes and No.

dataframe$class <- as.factor(ifelse(dataframe$class >= 1, 'Yes', 'No'))
dataframe <- data.frame(dataframe)
head(dataframe)

##   variance skewness curtosis  entropy class
## 1  3.62160   8.6661  -2.8073 -0.44699    No
## 2  4.54590   8.1674  -2.4586 -1.46210    No
## 3  3.86600  -2.6383   1.9242  0.10645    No
## 4  3.45660   9.5228  -4.0112 -3.59440    No
## 5  0.32924  -4.4552   4.5718 -0.98880    No
## 6  4.36840   9.6718  -3.9606 -3.16250    No

Spliting the dataset into train and test.

index <- createDataPartition(dataframe$class, p=0.75, list=FALSE)
trainSet <- dataframe[ index,]
testSet <- dataframe[-index,]

Feature selection using rfe in caret

control <- rfeControl(functions = rfFuncs,
  method = "repeatedcv",
  repeats = 3,
  verbose = FALSE)

outcomeName <- 'class'
predictors <- c("variance", "skewness", "curtosis", "entropy")

Model Training: Random forest

banknote_rf <- train(trainSet[,predictors],trainSet[,outcomeName],method='rf')

Model Training: neural network

banknote_nnet <- train(trainSet[,predictors],trainSet[,outcomeName],method='nnet')

Now we need to predict the outcome on testSet using the trained models

Predictions on testSet in probabilities

predictions_rf <- predict.train(object=banknote_rf,testSet[,predictors],type="prob")
predictions_nnet <- predict.train(object=banknote_nnet,testSet[,predictors],type="prob")

Predictions on testSet in raw form i.e in levels

predictions_rf_raw <- predict.train(object=banknote_rf,testSet[,predictors],type="raw")
predictions_nnet_raw <- predict.train(object=banknote_nnet,testSet[,predictors],type="raw")

Create the stack of prediction probabilities for the class of Yes

Stack_object <- list(predictions_rf$Yes, predictions_nnet$Yes)

Applying naming to the Stack_object

names(Stack_object) <- c("model_rf", "model_nnet")

Convet list object into dataframe

Stack_object_df <- data.frame(Stack_object)

nonet_ensemble

Now we need to apply the nonet_ensemble method by supplying list object and best model name as input. Note that We have not provided training or test outcome labels to compute the weights in the weighted average ensemble method, which is being used inside the none_ensemble. Thus it uses best models prediction to compute the weights in the weighted average ensemble.

prediction_nonet_raw <- nonet_ensemble(Stack_object, "model_nnet")

Convert probabilities into factor levels.

prediction_nonet <- as.factor(ifelse(prediction_nonet_raw >= "0.5", "Yes", "No"))

Evaluation Matrix: nonet

Here Confusion matrix is being used to evaluate the performance of nonet, rf and nnet.

nonet_eval <- confusionMatrix(prediction_nonet, testSet[,outcomeName])
nonet_eval_rf <- confusionMatrix(predictions_rf_raw,testSet[,outcomeName])
nonet_eval_nnet <- confusionMatrix(predictions_nnet_raw,testSet[,outcomeName])
nonet_eval_df <- data.frame(nonet_eval$table)
nonet_eval_rf_df <- data.frame(nonet_eval_rf$table)
nonet_eval_nnet_df <- data.frame(nonet_eval_nnet$table)

Result Plotting: nonet_plot

Results can be plotted using the nonet_plot function. nonet_plot is being designed to provided different plot_type options to the user so that one can plot different visualization based on their needs.

nonet_plot for the result of nonet_ensemble models predictions

plot_first <- nonet_plot(nonet_eval_df$Prediction, nonet_eval_df$Reference, nonet_eval_df, plot_type = "point")
plot_first

nonet_plot for the result of random forest model’s predictions

plot_second <- nonet_plot(nonet_eval_rf_df$Prediction, nonet_eval_rf_df$Reference, nonet_eval_rf_df, plot_type = "boxplot")
plot_second

nonet_plot for the result of neural network model’s predictions

plot_third <- nonet_plot(nonet_eval_nnet_df$Prediction, nonet_eval_nnet_df$Reference, nonet_eval_nnet_df, plot_type = "density")
plot_third

Conclusion

Above it can be seen that nonet_ensemble and nonet_plot can serve in a way that one do not need to worry about the outcome variables labels to compute the weights of weighted average ensemble solution.

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They may not be fully stable and should be used with caution. We make no claims about them.