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Markedness

Source: R/class-markedness.R
markedness.Rd

Markedness is defined as:

precision() + "inverse precision" - 1

where "inverse precision" is the proportion of true negatives among all predicted negatives. A related metric is Informedness, see the Details section for the relationship.

Usage

markedness(data, ...)

# S3 method for class 'data.frame'
markedness(
  data,
  truth,
  estimate,
  estimator = NULL,
  na_rm = TRUE,
  case_weights = NULL,
  event_level = yardstick_event_level(),
  ...
)

markedness_vec(
  truth,
  estimate,
  estimator = NULL,
  na_rm = TRUE,
  case_weights = NULL,
  event_level = yardstick_event_level(),
  ...
)

Arguments

data

Either a data.frame containing the columns specified by the truth and estimate arguments, or a table/matrix where the true class results should be in the columns of the table.

...

Not currently used.

truth

The column identifier for the true class results (that is a factor). This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For _vec() functions, a factor vector.

estimate

The column identifier for the predicted class results (that is also factor). As with truth this can be specified different ways but the primary method is to use an unquoted variable name. For _vec() functions, a factor vector.

estimator

One of: "binary", "macro", "macro_weighted", or "micro" to specify the type of averaging to be done. "binary" is only relevant for the two class case. The other three are general methods for calculating multiclass metrics. The default will automatically choose "binary" or "macro" based on estimate.

na_rm

A logical value indicating whether NA values should be stripped before the computation proceeds.

case_weights

The optional column identifier for case weights. This should be an unquoted column name that evaluates to a numeric column in data. For _vec() functions, a numeric vector, hardhat::importance_weights(), or hardhat::frequency_weights().

event_level

A single string. Either "first" or "second" to specify which level of truth to consider as the "event". This argument is only applicable when estimator = "binary". The default uses an internal helper that defaults to "first".

Value

A tibble with columns .metric, .estimator, and .estimate and 1 row of values.

For grouped data frames, the number of rows returned will be the same as the number of groups.

For markedness_vec(), a single numeric value (or NA).

Details

Suppose a 2x2 table with notation:

Reference
PredictedPositiveNegative
PositiveAB
NegativeCD

The formulas used here are:

$$\text{Precision} = \frac{A}{A + B}$$

$$\text{Inverse Precision} = \frac{D}{C + D}$$

$$\text{Markedness} = \text{Precision} + \text{Inverse Precision} - 1$$

Markedness is a metric that should be maximized. The output ranges from -1 to 1, with 1 indicating perfect predictions.

Markedness is to the predicted condition (precision and inverse precision) what Informedness (j_index()) is to the actual condition (sensitivity and specificity).

Relevant Level

There is no common convention on which factor level should automatically be considered the "event" or "positive" result when computing binary classification metrics. In yardstick, the default is to use the first level. To alter this, change the argument event_level to "second" to consider the last level of the factor the level of interest. For multiclass extensions involving one-vs-all comparisons (such as macro averaging), this option is ignored and the "one" level is always the relevant result.

Multiclass

Macro, micro, and macro-weighted averaging is available for this metric. The default is to select macro averaging if a truth factor with more than 2 levels is provided. Otherwise, a standard binary calculation is done. See vignette("multiclass", "yardstick") for more information.

References

Powers, David M W (2011). "Evaluation: From Precision, Recall and F-Score to ROC, Informedness, Markedness and Correlation". Journal of Machine Learning Technologies. 2 (1): 37-63.

See also

All class metrics

Other class metrics: accuracy(), bal_accuracy(), detection_prevalence(), f_meas(), fall_out(), j_index(), kap(), mcc(), miss_rate(), npv(), ppv(), precision(), recall(), roc_dist(), sens(), spec()

Examples

# Two class
data("two_class_example")
markedness(two_class_example, truth, predicted)
#> # A tibble: 1 × 3
#>   .metric    .estimator .estimate
#>   <chr>      <chr>          <dbl>
#> 1 markedness binary         0.680

# Multiclass
library(dplyr)
data(hpc_cv)

hpc_cv |>
  filter(Resample == "Fold01") |>
  markedness(obs, pred)
#> # A tibble: 1 × 3
#>   .metric    .estimator .estimate
#>   <chr>      <chr>          <dbl>
#> 1 markedness macro          0.543

# Groups are respected
hpc_cv |>
  group_by(Resample) |>
  markedness(obs, pred)
#> # A tibble: 10 × 4
#>    Resample .metric    .estimator .estimate
#>    <chr>    <chr>      <chr>          <dbl>
#>  1 Fold01   markedness macro          0.543
#>  2 Fold02   markedness macro          0.504
#>  3 Fold03   markedness macro          0.622
#>  4 Fold04   markedness macro          0.556
#>  5 Fold05   markedness macro          0.548
#>  6 Fold06   markedness macro          0.518
#>  7 Fold07   markedness macro          0.444
#>  8 Fold08   markedness macro          0.554
#>  9 Fold09   markedness macro          0.484
#> 10 Fold10   markedness macro          0.515

# Weighted macro averaging
hpc_cv |>
  group_by(Resample) |>
  markedness(obs, pred, estimator = "macro_weighted")
#> # A tibble: 10 × 4
#>    Resample .metric    .estimator     .estimate
#>    <chr>    <chr>      <chr>              <dbl>
#>  1 Fold01   markedness macro_weighted     0.592
#>  2 Fold02   markedness macro_weighted     0.579
#>  3 Fold03   markedness macro_weighted     0.657
#>  4 Fold04   markedness macro_weighted     0.568
#>  5 Fold05   markedness macro_weighted     0.583
#>  6 Fold06   markedness macro_weighted     0.553
#>  7 Fold07   markedness macro_weighted     0.502
#>  8 Fold08   markedness macro_weighted     0.587
#>  9 Fold09   markedness macro_weighted     0.506
#> 10 Fold10   markedness macro_weighted     0.547

# Vector version
markedness_vec(
  two_class_example$truth,
  two_class_example$predicted
)
#> [1] 0.6804811

# Making Class2 the "relevant" level
markedness_vec(
  two_class_example$truth,
  two_class_example$predicted,
  event_level = "second"
)
#> [1] 0.6804811