Description Usage Arguments Details Value Relevant Level Multiclass Implementation Author(s) References See Also Examples

View source: R/class-precision.R

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()`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
precision(data, ...)
## S3 method for class 'data.frame'
precision(
data,
truth,
estimate,
estimator = NULL,
na_rm = TRUE,
event_level = yardstick_event_level(),
...
)
precision_vec(
truth,
estimate,
estimator = NULL,
na_rm = TRUE,
event_level = yardstick_event_level(),
...
)
``` |

`data` |
Either a |

`...` |
Not currently used. |

`truth` |
The column identifier for the true class results
(that is a |

`estimate` |
The column identifier for the predicted class
results (that is also |

`estimator` |
One of: |

`na_rm` |
A |

`event_level` |
A single string. Either |

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.

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`

).

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.

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.

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+β^2) * precision * recall/((β^2 * precision)+recall)*

See the references for discussions of the statistics.

Max Kuhn

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

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()`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ```
# 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"
)
``` |

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