| Title: | Confusion Matrix Toolset |
|---|---|
| Description: | Takes the outputs of a 'caret' confusion matrix and allows for the quick conversion of these list items to lists. The intended usage is to allow the tool to work with the outputs of machine learning classification models. This tool works with classification problems for binary and multi-classification problems and allows for the record level conversion of the confusion matrix outputs. This is useful, as it allows quick conversion of these objects for storage in database systems and to track ML model performance over time. Traditionally, this approach has been used for highlighting model representation and feature slippage. |
| Authors: | Gary Hutson [aut, cre]
|
| Maintainer: | Gary Hutson <hutsons-hacks@outlook.com> |
| License: | MIT + file LICENSE |
| Version: | 1.0.4 |
| Built: | 2025-03-16 07:01:55 UTC |
| Source: | CRAN |
a confusion matrix object for binary classification machine learning problems.
binary_class_cm(train_labels, truth_labels, ...)binary_class_cm(train_labels, truth_labels, ...)
train_labels |
the classification labels from the training set |
truth_labels |
the testing set ground truth labels for comparison |
... |
function forwarding for additional 'caret' confusion matrix parameters to be passed such as mode="everything" and positive="class label" |
A list containing the outputs highlighted hereunder:
"confusion_matrix" a confusion matrix list item with all the associated confusion matrix statistics
"record_level_cm" a row by row data.frame version of the above output, to allow for storage in databases and row by row for tracking ML model performance
"cm_tbl" a confusion matrix raw table of the values in the matrix
"last_run"datetime object storing when the function was run
library(dplyr) library(ConfusionTableR) library(caret) library(tidyr) library(mlbench) # Load in the data data("BreastCancer", package = "mlbench") breast <- BreastCancer[complete.cases(BreastCancer), ] #Create a copy breast <- breast[, -1] breast <- breast[1:100,] breast$Class <- factor(breast$Class) # Create as factor for(i in 1:9) { breast[, i] <- as.numeric(as.character(breast[, i])) } #Perform train / test split on the data train_split_idx <- caret::createDataPartition(breast$Class, p = 0.75, list = FALSE) train <- breast[train_split_idx, ] test <- breast[-train_split_idx, ] rf_fit <- caret::train(Class ~ ., data=train, method="rf") #Make predictions to expose class labels preds <- predict(rf_fit, newdata=test, type="raw") predicted <- cbind(data.frame(class_preds=preds), test) #ConfusionTableR to produce record level output cm <- ConfusionTableR::binary_class_cm(predicted$class_preds,predicted$Class) # Other modes here are mode="prec_recall", mode="sens_spec" and mode="everything" # Record level output cm$record_level_cm #Primed for storage in a database table # List confusion matrix cm$confusion_matrixlibrary(dplyr) library(ConfusionTableR) library(caret) library(tidyr) library(mlbench) # Load in the data data("BreastCancer", package = "mlbench") breast <- BreastCancer[complete.cases(BreastCancer), ] #Create a copy breast <- breast[, -1] breast <- breast[1:100,] breast$Class <- factor(breast$Class) # Create as factor for(i in 1:9) { breast[, i] <- as.numeric(as.character(breast[, i])) } #Perform train / test split on the data train_split_idx <- caret::createDataPartition(breast$Class, p = 0.75, list = FALSE) train <- breast[train_split_idx, ] test <- breast[-train_split_idx, ] rf_fit <- caret::train(Class ~ ., data=train, method="rf") #Make predictions to expose class labels preds <- predict(rf_fit, newdata=test, type="raw") predicted <- cbind(data.frame(class_preds=preds), test) #ConfusionTableR to produce record level output cm <- ConfusionTableR::binary_class_cm(predicted$class_preds,predicted$Class) # Other modes here are mode="prec_recall", mode="sens_spec" and mode="everything" # Record level output cm$record_level_cm #Primed for storage in a database table # List confusion matrix cm$confusion_matrix
a confusion matrix object for binary classification machine learning problems. Returns a plot to visualise the important statistics derived from a confusion matrix, see: https://machinelearningmastery.com/confusion-matrix-machine-learning/.
binary_visualiseR( train_labels, truth_labels, class_label1 = "Class Negative", class_label2 = "Class Positive", quadrant_col1 = "#3F97D0", quadrant_col2 = "#F7AD50", custom_title = "Confusion matrix", info_box_title = "Confusion matrix statistics", text_col = "black", round_dig = 2, cm_stat_size = 1.4, cm_stat_lbl_size = 1.5, ... )binary_visualiseR( train_labels, truth_labels, class_label1 = "Class Negative", class_label2 = "Class Positive", quadrant_col1 = "#3F97D0", quadrant_col2 = "#F7AD50", custom_title = "Confusion matrix", info_box_title = "Confusion matrix statistics", text_col = "black", round_dig = 2, cm_stat_size = 1.4, cm_stat_lbl_size = 1.5, ... )
train_labels |
the classification labels from the training set |
truth_labels |
the testing set ground truth labels for comparison |
class_label1 |
classification label 1 i.e. readmission into hospital |
class_label2 |
classification label 2 i.e. not a readmission into hospital |
quadrant_col1 |
colour of the first quadrant - specified as hexadecimal |
quadrant_col2 |
colour of the second quadrant - specified as hexadecimal |
custom_title |
title of the confusion matrix plot |
info_box_title |
title of the confusion matrix statistics box |
text_col |
the colour of the text |
round_dig |
rounding options |
cm_stat_size |
the cex size of the statistics box label |
cm_stat_lbl_size |
the cex size of the label in the statistics box |
... |
function forwarding to the confusion matrix object to pass additional args, such as positive = "Class label" |
returns a visual of a Confusion Matrix output
library(dplyr) library(ConfusionTableR) library(caret) library(tidyr) library(mlbench) # Load in the data data("BreastCancer", package = "mlbench") breast <- BreastCancer[complete.cases(BreastCancer), ] #Create a copy breast <- breast[, -1] breast <- breast[1:100,] breast$Class <- factor(breast$Class) # Create as factor for(i in 1:9) { breast[, i] <- as.numeric(as.character(breast[, i])) } #Perform train / test split on the data train_split_idx <- caret::createDataPartition(breast$Class, p = 0.75, list = FALSE) train <- breast[train_split_idx, ] test <- breast[-train_split_idx, ] rf_fit <- caret::train(Class ~ ., data=train, method="rf") #Make predictions to expose class labels preds <- predict(rf_fit, newdata=test, type="raw") predicted <- cbind(data.frame(class_preds=preds), test) # Create the visual ConfusionTableR::binary_visualiseR(predicted$class_preds, predicted$Class)library(dplyr) library(ConfusionTableR) library(caret) library(tidyr) library(mlbench) # Load in the data data("BreastCancer", package = "mlbench") breast <- BreastCancer[complete.cases(BreastCancer), ] #Create a copy breast <- breast[, -1] breast <- breast[1:100,] breast$Class <- factor(breast$Class) # Create as factor for(i in 1:9) { breast[, i] <- as.numeric(as.character(breast[, i])) } #Perform train / test split on the data train_split_idx <- caret::createDataPartition(breast$Class, p = 0.75, list = FALSE) train <- breast[train_split_idx, ] test <- breast[-train_split_idx, ] rf_fit <- caret::train(Class ~ ., data=train, method="rf") #Make predictions to expose class labels preds <- predict(rf_fit, newdata=test, type="raw") predicted <- cbind(data.frame(class_preds=preds), test) # Create the visual ConfusionTableR::binary_visualiseR(predicted$class_preds, predicted$Class)
This function has been designed to encode multiple columns at once and allows the user to specify whether to drop the reference columns or retain them in the data
dummy_encoder(df, columns, map_fn = furrr::future_map, remove_original = TRUE)dummy_encoder(df, columns, map_fn = furrr::future_map, remove_original = TRUE)
df |
- data.frame object to pass to the function |
columns |
- vector of columns to be encoded for dummy encoding |
map_fn |
- choice of mapping function purrr:map or furr::future_map accepted |
remove_original |
- remove the variables that the dummy encodings are based off |
A tibble containing the dummy encodings
## Not run: #Use the NHSR stranded dataset df <- NHSRdatasets::stranded_data #Create a function to select categorical variables sep_categorical <- function(df){ cats <- df %>% dplyr::select_if(is.character) return(cats) } cats <- sep_categorical(df) %>% dplyr::select(-c(admit_date)) #Dummy encoding columns_vector <- c(names(cats)) dummy_encodings <- dummy_encoder(cats, columns_vector) glimpse(dummy_encodings) ## End(Not run)## Not run: #Use the NHSR stranded dataset df <- NHSRdatasets::stranded_data #Create a function to select categorical variables sep_categorical <- function(df){ cats <- df %>% dplyr::select_if(is.character) return(cats) } cats <- sep_categorical(df) %>% dplyr::select(-c(admit_date)) #Dummy encoding columns_vector <- c(names(cats)) dummy_encodings <- dummy_encoder(cats, columns_vector) glimpse(dummy_encodings) ## End(Not run)
a confusion matrix object for multiple outcome classification machine learning problems.
multi_class_cm(train_labels, truth_labels, ...)multi_class_cm(train_labels, truth_labels, ...)
train_labels |
the classification labels from the training set |
truth_labels |
the testing set ground truth labels for comparison |
... |
function forwarding for passing mode and other parameters to 'caret' confusionMatrix |
A list containing the outputs highlighted hereunder:
"confusion_matrix" a confusion matrix list item with all the associated confusion matrix statistics
"record_level_cm" a row by row data.frame version of the above output, to allow for storage in databases and row by row for tracking ML model performance
"cm_tbl" a confusion matrix raw table of the values in the matrix
"last_run"datetime object storing when the function was run
# Get the IRIS data as this is a famous multi-classification problem library(caret) library(ConfusionTableR) library(randomForest) df <- iris df <- na.omit(df) table(iris$Species) # Create a training / test split train_split_idx <- caret::createDataPartition(df$Species, p = 0.75, list = FALSE) # Here we define a split index and we are now going to use a multiclass ML model to fit the data train <- df[train_split_idx, ] test <- df[-train_split_idx, ] # Fit a random forest model on the data rf_model <- caret::train(Species ~ .,data = df,method = "rf", metric = "Accuracy") # Predict the values on the test hold out set rf_class <- predict(rf_model, newdata = test, type = "raw") predictions <- cbind(data.frame(train_preds=rf_class, test$Species)) # Use ConfusionTableR to create a row level output cm <- ConfusionTableR::multi_class_cm(predictions$train_preds, predictions$test.Species) # Create the row level output cm_rl <- cm$record_level_cm print(cm_rl) #Expose the original confusion matrix list cm_orig <- cm$confusion_matrix print(cm_orig)# Get the IRIS data as this is a famous multi-classification problem library(caret) library(ConfusionTableR) library(randomForest) df <- iris df <- na.omit(df) table(iris$Species) # Create a training / test split train_split_idx <- caret::createDataPartition(df$Species, p = 0.75, list = FALSE) # Here we define a split index and we are now going to use a multiclass ML model to fit the data train <- df[train_split_idx, ] test <- df[-train_split_idx, ] # Fit a random forest model on the data rf_model <- caret::train(Species ~ .,data = df,method = "rf", metric = "Accuracy") # Predict the values on the test hold out set rf_class <- predict(rf_model, newdata = test, type = "raw") predictions <- cbind(data.frame(train_preds=rf_class, test$Species)) # Use ConfusionTableR to create a row level output cm <- ConfusionTableR::multi_class_cm(predictions$train_preds, predictions$test.Species) # Create the row level output cm_rl <- cm$record_level_cm print(cm_rl) #Expose the original confusion matrix list cm_orig <- cm$confusion_matrix print(cm_orig)