mlr3measures (version 1.0.0)

ce: Classification Error

Description

Measure to compare true observed labels with predicted labels in multiclass classification tasks.

Usage

ce(truth, response, sample_weights = NULL, ...)

Value

Performance value as numeric(1).

Arguments

truth

(factor())
True (observed) labels. Must have the same levels and length as response.

response

(factor())
Predicted response labels. Must have the same levels and length as truth.

sample_weights

(numeric())
Vector of non-negative and finite sample weights. Must have the same length as truth. The vector gets automatically normalized to sum to one. Defaults to equal sample weights.

...

(any)
Additional arguments. Currently ignored.

Meta Information

• Type: "classif"

• Range: \([0, 1]\)

• Minimize: TRUE

• Required prediction: response

Details

The Classification Error is defined as $$ \frac{1}{n} \sum_{i=1}^n w_i \mathbf{1} \left( t_i \neq r_i \right), $$ where \(w_i\) are normalized weights for each observation \(x_i\).

See Also

Other Classification Measures: acc(), bacc(), logloss(), mauc_aunu(), mbrier(), mcc(), zero_one()

Examples

set.seed(1)
lvls = c("a", "b", "c")
truth = factor(sample(lvls, 10, replace = TRUE), levels = lvls)
response = factor(sample(lvls, 10, replace = TRUE), levels = lvls)
ce(truth, response)