precision function

Precision

These functions calculate the precision() of a measurement system for finding relevant documents compared to reference results (the truth regarding relevance). Highly related functions are recall()

and f_meas().

precision(data, ...)

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

precision_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".

Returns

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 precision_vec(), a single numeric value (or NA).

Details

The precision is the percentage of predicted truly relevant results of the total number of predicted relevant results and characterizes the "purity in retrieval performance" (Buckland and Gey, 1994).

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

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.

Implementation

Suppose a 2x2 table with notation:

	Reference
Predicted	Relevant	Irrelevant
Relevant	A	B
Irrelevant	C	D

The formulas used here are:

$$recall = A/(A+C)$$ $$precision = A/(A+B)$$ $$F_{meas} = (1+\beta^2) * precision * recall/((\beta^2 * precision)+recall)$$

See the references for discussions of the statistics.

Examples

# Two class
data("two_class_example")
precision(two_class_example, truth, predicted)

# Multiclass
library(dplyr)
data(hpc_cv)

hpc_cv %>%
  filter(Resample == "Fold01") %>%
  precision(obs, pred)

# Groups are respected
hpc_cv %>%
  group_by(Resample) %>%
  precision(obs, pred)

# Weighted macro averaging
hpc_cv %>%
  group_by(Resample) %>%
  precision(obs, pred, estimator = "macro_weighted")

# Vector version
precision_vec(
  two_class_example$truth,
  two_class_example$predicted
)

# Making Class2 the "relevant" level
precision_vec(
  two_class_example$truth,
  two_class_example$predicted,
  event_level = "second"
)

References

Buckland, M., & Gey, F. (1994). The relationship between Recall and Precision. Journal of the American Society for Information Science, 45(1), 12-19.

Powers, D. (2007). Evaluation: From Precision, Recall and F Factor to ROC, Informedness, Markedness and Correlation. Technical Report SIE-07-001, Flinders University

See Also

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

Other relevance metrics: f_meas(), recall()

Author(s)

Max Kuhn