predicted.musicians {cvms} | 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:

- Classifier
The applied classifier. One of

`"nnet_multinom"`

,`"randomForest"`

, and`"e1071_svm"`

.- Fold Column
The fold column name. Each is a unique 5-fold split. One of

`".folds_1"`

,`".folds_2"`

, and`".folds_3"`

.- Fold
The fold.

`1`

to`5`

.- ID
Musician identifier, 60 levels

- Target
The actual class of the musician. One of

`"A"`

,`"B"`

,`"C"`

, and`"D"`

.- A
The probability of class

`"A"`

.- B
The probability of class

`"B"`

.- C
The probability of class

`"C"`

.- D
The probability of class

`"D"`

.- Predicted Class
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]])
```

[Package *cvms* version 1.3.3 Index]