Title: | Weighted Average Ensemble without Training Labels |
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Description: | It provides ensemble capabilities to supervised and unsupervised learning models predictions without using training labels. It decides the relative weights of the different models predictions by using best models predictions as response variable and rest of the mo. User can decide the best model, therefore, It provides freedom to user to ensemble models based on their design solutions. |
Authors: | Aviral Vijay [aut, cre], Sameer Mahajan [aut] |
Maintainer: | Aviral Vijay <[email protected]> |
License: | MIT + file LICENSE |
Version: | 0.4.0 |
Built: | 2024-10-22 04:47:15 UTC |
Source: | https://github.com/cran/nonet |
Bank Note Authentication Data Set is used to show the functionality of nonet package. This Data Set has below attribute information as variance of Wavelet Transformed image (continuous), skewness of Wavelet Transformed image (continuous), curtosis of Wavelet Transformed image (continuous),entropy of image (continuous), class (integer).
data("banknote_authentication")
data("banknote_authentication")
A data frame with 1372 observations on the following 5 variables.
variance
a numeric vector
skewness
a numeric vector
curtosis
a numeric vector
entropy
a numeric vector
class
a numeric vector
Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tool were used to extract features from images.
This DataSet is fetched from UCI Website. URL is https://archive.ics.uci.edu/ml/datasets/banknote+authentication
Owner of database: Volker Lohweg (University of Applied Sciences, Ostwestfalen-Lippe, volker.lohweg '@' hs-owl.de) Donor of database: Helene Dörksen (University of Applied Sciences, Ostwestfalen-Lippe, helene.doerksen '@' hs-owl.de) Date received: August, 2012
data(banknote_authentication) dataframe <- data.frame(banknote_authentication) head(dataframe) str(banknote_authentication)
data(banknote_authentication) dataframe <- data.frame(banknote_authentication) head(dataframe) str(banknote_authentication)
Ensemble Prediction without using training labels
nonet_ensemble(object, best_modelname)
nonet_ensemble(object, best_modelname)
object |
prediction_list object, as from 'tune_models' |
best_modelname |
Best model name is one which performance better while evaluating using any evaluation matrix like confusion matrix. |
A list of ensembled predictions. You can evaluate the performance of ensembled prediction using the evaulation matrix as Confusion matrix or AUROC.
# nonet_ensemble functionality can be explained via below example # Setup library(caret) library(nonet) library(rlist) # Load Data dataframe <- data.frame(banknote_authentication[600:900, ]) dataframe$class <- as.factor(ifelse(dataframe$class >= 1, 'Yes', 'No')) # First Model # Spliting into train and test index <- createDataPartition(dataframe$class, p=0.75, list=FALSE) trainSet <- dataframe[ index,] testSet <- dataframe[-index,] #Feature selection control <- rfeControl(functions = rfFuncs, method = "repeatedcv", repeats = 1, verbose = FALSE) outcomeName <- 'class' predictors <- c("variance", "skewness") banknote_rf <- train(trainSet[,predictors],trainSet[,outcomeName],method='rf') preds_rf_first <- predict.train(object=banknote_rf,testSet[,predictors],type="prob") preds_rf_first_raw <- predict.train(object=banknote_rf,testSet[,predictors],type="raw") # Second Model # Spliting into train and test index <- createDataPartition(dataframe$class, p=0.75, list=FALSE) trainSet <- dataframe[ index,] testSet <- dataframe[-index,] #Feature selection control <- rfeControl(functions = rfFuncs, method = "repeatedcv", repeats = 2, verbose = FALSE) outcomeName <- 'class' predictors <- c("curtosis", "entropy") banknote_rf <- train(trainSet[,predictors],trainSet[,outcomeName],method='rf') preds_rf_second <- predict.train(object=banknote_rf,testSet[,predictors],type="prob") preds_rf_second_raw <- predict.train(object=banknote_rf,testSet[,predictors],type="raw") Stack_object <- list(preds_rf_first$Yes, preds_rf_second$Yes) names(Stack_object) <- c("model_rf_first", "model_rf_second") # Prediction using nonet_ensemble function prediction_nonet <- nonet_ensemble(Stack_object, "model_rf_second")
# nonet_ensemble functionality can be explained via below example # Setup library(caret) library(nonet) library(rlist) # Load Data dataframe <- data.frame(banknote_authentication[600:900, ]) dataframe$class <- as.factor(ifelse(dataframe$class >= 1, 'Yes', 'No')) # First Model # Spliting into train and test index <- createDataPartition(dataframe$class, p=0.75, list=FALSE) trainSet <- dataframe[ index,] testSet <- dataframe[-index,] #Feature selection control <- rfeControl(functions = rfFuncs, method = "repeatedcv", repeats = 1, verbose = FALSE) outcomeName <- 'class' predictors <- c("variance", "skewness") banknote_rf <- train(trainSet[,predictors],trainSet[,outcomeName],method='rf') preds_rf_first <- predict.train(object=banknote_rf,testSet[,predictors],type="prob") preds_rf_first_raw <- predict.train(object=banknote_rf,testSet[,predictors],type="raw") # Second Model # Spliting into train and test index <- createDataPartition(dataframe$class, p=0.75, list=FALSE) trainSet <- dataframe[ index,] testSet <- dataframe[-index,] #Feature selection control <- rfeControl(functions = rfFuncs, method = "repeatedcv", repeats = 2, verbose = FALSE) outcomeName <- 'class' predictors <- c("curtosis", "entropy") banknote_rf <- train(trainSet[,predictors],trainSet[,outcomeName],method='rf') preds_rf_second <- predict.train(object=banknote_rf,testSet[,predictors],type="prob") preds_rf_second_raw <- predict.train(object=banknote_rf,testSet[,predictors],type="raw") Stack_object <- list(preds_rf_first$Yes, preds_rf_second$Yes) names(Stack_object) <- c("model_rf_first", "model_rf_second") # Prediction using nonet_ensemble function prediction_nonet <- nonet_ensemble(Stack_object, "model_rf_second")
Plot the predictions or results of nonet_ensemble
nonet_plot(x, y, dataframe, plot_type = NULL, nonet_size = 20, nonet_alpha = 0.3, nonet_bins = 25)
nonet_plot(x, y, dataframe, plot_type = NULL, nonet_size = 20, nonet_alpha = 0.3, nonet_bins = 25)
x |
x axis variable name or histogram entity name |
y |
y axis variable name |
dataframe |
dataframe which is used for plotting purpose. |
plot_type |
type of plot, if not provided it takes "NULL" |
nonet_size |
size of plot need to feed in ggplot |
nonet_alpha |
value of alpha for ggplot |
nonet_bins |
number of bins for histogram |
plotted for the plot results provided as input.
# nonet_plot functionality can be explained via below example # Setup library(caret) library(nonet) library(ggplot2) # Load Data dataframe <- data.frame(banknote_authentication[600:900, ]) dataframe$class <- as.factor(ifelse(dataframe$class >= 1, 'Yes', 'No')) # Spliting into train and test index <- createDataPartition(dataframe$class, p=0.75, list=FALSE) trainSet <- dataframe[ index,] testSet <- dataframe[-index,] # Feature selection control <- rfeControl(functions = rfFuncs, method = "repeatedcv", repeats = 2, verbose = FALSE) outcomeName <- 'class' predictors <- c("curtosis", "entropy") # Model Training & predictions banknote_rf <- train(trainSet[,predictors],trainSet[,outcomeName],method='rf') predictions_rf_raw <- predict.train(object=banknote_rf,testSet[,predictors],type="raw") # Results nonet_eval_rf <- confusionMatrix(predictions_rf_raw,testSet[,outcomeName]) eval_rf_df <- data.frame(nonet_eval_rf$table) nonet_plot(eval_rf_df$Prediction, eval_rf_df$Reference, eval_rf_df, plot_type = "point")
# nonet_plot functionality can be explained via below example # Setup library(caret) library(nonet) library(ggplot2) # Load Data dataframe <- data.frame(banknote_authentication[600:900, ]) dataframe$class <- as.factor(ifelse(dataframe$class >= 1, 'Yes', 'No')) # Spliting into train and test index <- createDataPartition(dataframe$class, p=0.75, list=FALSE) trainSet <- dataframe[ index,] testSet <- dataframe[-index,] # Feature selection control <- rfeControl(functions = rfFuncs, method = "repeatedcv", repeats = 2, verbose = FALSE) outcomeName <- 'class' predictors <- c("curtosis", "entropy") # Model Training & predictions banknote_rf <- train(trainSet[,predictors],trainSet[,outcomeName],method='rf') predictions_rf_raw <- predict.train(object=banknote_rf,testSet[,predictors],type="raw") # Results nonet_eval_rf <- confusionMatrix(predictions_rf_raw,testSet[,outcomeName]) eval_rf_df <- data.frame(nonet_eval_rf$table) nonet_plot(eval_rf_df$Prediction, eval_rf_df$Reference, eval_rf_df, plot_type = "point")