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Miss rate (False Negative Rate)

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

These functions calculate the miss rate (false negative rate) of a measurement system compared to a reference result (the "truth" or gold standard). Miss rate is defined as 1 - sensitivity, or equivalently, the proportion of positives that are incorrectly classified as negatives.

Usage

miss_rate(data, ...)

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

miss_rate_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 miss_rate_vec(), a single numeric value (or NA).

Details

Miss rate is also known as the false negative rate (FNR) or the probability of miss.

When the denominator of the calculation is 0, miss rate is undefined. This happens when both # true_positive = 0 and # false_negative = 0 are true, which means that there were no events. When computing binary miss rate, a NA value will be returned with a warning. When computing multiclass miss rate, the individual NA values will be removed, and the computation will proceed, with a warning.

Suppose a 2x2 table with notation:

Reference
PredictedPositiveNegative
PositiveAB
NegativeCD

The formula used here is:

$$\text{Miss rate} = \frac{C}{A + C}$$

Miss rate is a metric that should be minimized. The output ranges from 0 to 1, with 0 indicating that all actual positives were correctly predicted as positive (no false negatives).

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.

See also

All class metrics

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

Other sensitivity metrics: fall_out(), npv(), ppv(), sens(), spec()

Examples

# Two class
data("two_class_example")
miss_rate(two_class_example, truth, predicted)
#> # A tibble: 1 × 3
#>   .metric   .estimator .estimate
#>   <chr>     <chr>          <dbl>
#> 1 miss_rate binary         0.120

# Multiclass
library(dplyr)
data(hpc_cv)

hpc_cv |>
  filter(Resample == "Fold01") |>
  miss_rate(obs, pred)
#> # A tibble: 1 × 3
#>   .metric   .estimator .estimate
#>   <chr>     <chr>          <dbl>
#> 1 miss_rate macro          0.452

# Groups are respected
hpc_cv |>
  group_by(Resample) |>
  miss_rate(obs, pred)
#> # A tibble: 10 × 4
#>    Resample .metric   .estimator .estimate
#>    <chr>    <chr>     <chr>          <dbl>
#>  1 Fold01   miss_rate macro          0.452
#>  2 Fold02   miss_rate macro          0.459
#>  3 Fold03   miss_rate macro          0.366
#>  4 Fold04   miss_rate macro          0.430
#>  5 Fold05   miss_rate macro          0.450
#>  6 Fold06   miss_rate macro          0.460
#>  7 Fold07   miss_rate macro          0.469
#>  8 Fold08   miss_rate macro          0.416
#>  9 Fold09   miss_rate macro          0.432
#> 10 Fold10   miss_rate macro          0.463

# Weighted macro averaging
hpc_cv |>
  group_by(Resample) |>
  miss_rate(obs, pred, estimator = "macro_weighted")
#> # A tibble: 10 × 4
#>    Resample .metric   .estimator     .estimate
#>    <chr>    <chr>     <chr>              <dbl>
#>  1 Fold01   miss_rate macro_weighted     0.274
#>  2 Fold02   miss_rate macro_weighted     0.288
#>  3 Fold03   miss_rate macro_weighted     0.242
#>  4 Fold04   miss_rate macro_weighted     0.288
#>  5 Fold05   miss_rate macro_weighted     0.288
#>  6 Fold06   miss_rate macro_weighted     0.303
#>  7 Fold07   miss_rate macro_weighted     0.325
#>  8 Fold08   miss_rate macro_weighted     0.279
#>  9 Fold09   miss_rate macro_weighted     0.327
#> 10 Fold10   miss_rate macro_weighted     0.301

# Vector version
miss_rate_vec(
  two_class_example$truth,
  two_class_example$predicted
)
#> [1] 0.120155

# Making Class2 the "relevant" level
miss_rate_vec(
  two_class_example$truth,
  two_class_example$predicted,
  event_level = "second"
)
#> [1] 0.2066116