Description Usage Arguments Value Examples
View source: R/binaryVisualiseR.R
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/.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | 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)
|
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