predicted.musicians | R Documentation |
Predictions by 3 classifiers of the 4 classes in the
musicians
dataset.
Obtained with 5-fold stratified cross-validation (3 repetitions).
The three classifiers were fit using nnet::multinom
,
randomForest::randomForest
, and e1071::svm
.
A data.frame
with 540
rows and 10
variables:
The applied classifier.
One of "nnet_multinom"
, "randomForest"
, and "e1071_svm"
.
The fold column name. Each is a unique 5-fold split.
One of ".folds_1"
, ".folds_2"
, and ".folds_3"
.
The fold. 1
to 5
.
Musician identifier, 60 levels
The actual class of the musician.
One of "A"
, "B"
, "C"
, and "D"
.
The probability of class "A"
.
The probability of class "B"
.
The probability of class "C"
.
The probability of class "D"
.
The predicted class. The argmax of the four probability columns.
Used formula: "Class ~ Height + Age + Drums + Bass + Guitar + Keys + Vocals"
Ludvig Renbo Olsen, r-pkgs@ludvigolsen.dk
musicians
# Attach packages
library(cvms)
library(dplyr)
# Evaluate each fold column
predicted.musicians %>%
dplyr::group_by(Classifier, `Fold Column`) %>%
evaluate(target_col = "Target",
prediction_cols = c("A", "B", "C", "D"),
type = "multinomial")
# Overall ID evaluation
# I.e. if we average all 9 sets of predictions,
# how well did we predict the targets?
overall_id_eval <- predicted.musicians %>%
evaluate(target_col = "Target",
prediction_cols = c("A", "B", "C", "D"),
type = "multinomial",
id_col = "ID")
overall_id_eval
# Plot the confusion matrix
plot_confusion_matrix(overall_id_eval$`Confusion Matrix`[[1]])
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