metric_cohen_kappa: Computes Kappa score between two raters

In EagerAI/tfaddons: Interface to 'TensorFlow SIG Addons'

Computes Kappa score between two raters

Description

Computes Kappa score between two raters

Usage

metric_cohen_kappa(
  num_classes,
  name = "cohen_kappa",
  weightage = NULL,
  sparse_labels = FALSE,
  regression = FALSE,
  dtype = NULL
)

Arguments

num_classes	Number of unique classes in your dataset.
name	(optional) String name of the metric instance
weightage	(optional) Weighting to be considered for calculating kappa statistics. A valid value is one of [None, 'linear', 'quadratic']. Defaults to 'NULL'
sparse_labels	(bool) Valid only for multi-class scenario. If True, ground truth labels are expected tp be integers and not one-hot encoded
regression	(bool) If set, that means the problem is being treated as a regression problem where you are regressing the predictions. **Note:** If you are regressing for the values, the the output layer should contain a single unit.
dtype	(optional) Data type of the metric result. Defaults to 'NULL'

Details

The score lies in the range [-1, 1]. A score of -1 represents complete disagreement between two raters whereas a score of 1 represents complete agreement between the two raters. A score of 0 means agreement by chance.

Value

Input tensor or list of input tensors.

Examples

## Not run: 
model = keras_model_sequential() %>%
  layer_dense(units = 10, input_shape = ncol(iris) - 1,activation = activation_lisht) %>%
  layer_dense(units = 3)

model %>% compile(loss = 'categorical_crossentropy',
                  optimizer = optimizer_radam(),
                  metrics = metric_cohen_kappa(3))

## End(Not run)

EagerAI/tfaddons documentation built on April 3, 2022, 11:42 p.m.