Package 'probably'

Description

This function is similar to make_class_pred(), but is useful when you have a large number of class probability columns and want to use tidyselect helpers. It appends the new class_pred vector as a column on the original data frame.

Usage

append_class_pred(
  .data,
  ...,
  levels,
  ordered = FALSE,
  min_prob = 1/length(levels),
  name = ".class_pred"
)

Arguments

Value

.data with an extra class_pred column appended onto it.

Examples

# The following two examples are equivalent and demonstrate
# the helper, append_class_pred()

library(dplyr)

species_probs %>%
  mutate(
    .class_pred = make_class_pred(
      .pred_bobcat, .pred_coyote, .pred_gray_fox,
      levels = levels(Species),
      min_prob = .5
    )
  )

lvls <- levels(species_probs$Species)

append_class_pred(
  .data = species_probs,
  contains(".pred_"),
  levels = lvls,
  min_prob = .5
)

Description

as_class_pred() provides coercion to class_pred from other existing objects.

Usage

as_class_pred(x, which = integer(), equivocal = "[EQ]")

Arguments

Examples

x <- factor(c("Yes", "No", "Yes", "Yes"))
as_class_pred(x)

Description

Boosted regression trees predictions

Details

These data have a set of holdout predictions from 10-fold cross-validation and a separate collection of test set predictions from the same boosted tree model. The data were generated using the sim_regression function in the modeldata package.

Value

Examples

data(boosting_predictions_oob)
str(boosting_predictions_oob)
str(boosting_predictions_test)

Description

For user-defined lower_limit and/or upper_limit bound, ensure that the values in the .pred column are coerced to these bounds.

Usage

bound_prediction(
  x,
  lower_limit = -Inf,
  upper_limit = Inf,
  call = rlang::current_env()
)

Arguments

Value

x with potentially adjusted values.

Examples

data(solubility_test, package = "yardstick")

names(solubility_test) <- c("solubility", ".pred")

bound_prediction(solubility_test, lower_limit = -1)

Description

Applies a calibration to a set of existing predictions

Usage

cal_apply(.data, object, pred_class = NULL, parameters = NULL, ...)

## S3 method for class 'data.frame'
cal_apply(.data, object, pred_class = NULL, parameters = NULL, ...)

## S3 method for class 'tune_results'
cal_apply(.data, object, pred_class = NULL, parameters = NULL, ...)

## S3 method for class 'cal_object'
cal_apply(.data, object, pred_class = NULL, parameters = NULL, ...)

Arguments

Details

cal_apply() currently supports data.frames only. It extracts the truth and the estimate columns names from the calibration object.

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_estimate_beta(), cal_estimate_isotonic(), cal_estimate_isotonic_boot(), cal_estimate_linear(), cal_estimate_logistic(), cal_estimate_multinomial()

Examples

# ------------------------------------------------------------------------------
# classification example

w_calibration <- cal_estimate_logistic(segment_logistic, Class)

cal_apply(segment_logistic, w_calibration)

Description

Uses a Beta calibration model to calculate new probabilities

Usage

cal_estimate_beta(
  .data,
  truth = NULL,
  shape_params = 2,
  location_params = 1,
  estimate = dplyr::starts_with(".pred_"),
  parameters = NULL,
  ...
)

## S3 method for class 'data.frame'
cal_estimate_beta(
  .data,
  truth = NULL,
  shape_params = 2,
  location_params = 1,
  estimate = dplyr::starts_with(".pred_"),
  parameters = NULL,
  ...,
  .by = NULL
)

## S3 method for class 'tune_results'
cal_estimate_beta(
  .data,
  truth = NULL,
  shape_params = 2,
  location_params = 1,
  estimate = dplyr::starts_with(".pred_"),
  parameters = NULL,
  ...
)

## S3 method for class 'grouped_df'
cal_estimate_beta(
  .data,
  truth = NULL,
  shape_params = 2,
  location_params = 1,
  estimate = NULL,
  parameters = NULL,
  ...
)

Arguments

Details

This function uses the betacal::beta_calibration() function, and retains the resulting model.

Multiclass Extension

This method is designed to work with two classes. For multiclass, it creates a set of "one versus all" calibrations for each class. After they are applied to the data, the probability estimates are re-normalized to add to one. This final step might compromise the calibration.

References

Meelis Kull, Telmo M. Silva Filho, Peter Flach "Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration," Electronic Journal of Statistics 11(2), 5052-5080, (2017)

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_validate_beta()

Examples

if (rlang::is_installed("betacal")) {
 # It will automatically identify the probability columns
  # if passed a model fitted with tidymodels
  cal_estimate_beta(segment_logistic, Class)
}

Description

Uses an Isotonic regression model to calibrate model predictions.

Usage

cal_estimate_isotonic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  parameters = NULL,
  ...
)

## S3 method for class 'data.frame'
cal_estimate_isotonic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  parameters = NULL,
  ...,
  .by = NULL
)

## S3 method for class 'tune_results'
cal_estimate_isotonic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  parameters = NULL,
  ...
)

## S3 method for class 'grouped_df'
cal_estimate_isotonic(
  .data,
  truth = NULL,
  estimate = NULL,
  parameters = NULL,
  ...
)

Arguments

Details

This function uses stats::isoreg() to create obtain the calibration values for binary classification or numeric regression.

Multiclass Extension

This method is designed to work with two classes. For multiclass, it creates a set of "one versus all" calibrations for each class. After they are applied to the data, the probability estimates are re-normalized to add to one. This final step might compromise the calibration.

References

Zadrozny, Bianca and Elkan, Charles. (2002). Transforming Classifier Scores into Accurate Multiclass Probability Estimates. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_validate_isotonic()

Examples

# ------------------------------------------------------------------------------
# Binary Classification

# It will automatically identify the probability columns
# if passed a model fitted with tidymodels
cal_estimate_isotonic(segment_logistic, Class)

# Specify the variable names in a vector of unquoted names
cal_estimate_isotonic(segment_logistic, Class, c(.pred_poor, .pred_good))

# dplyr selector functions are also supported
cal_estimate_isotonic(segment_logistic, Class, dplyr::starts_with(".pred_"))

# ------------------------------------------------------------------------------
# Regression (numeric outcomes)

cal_estimate_isotonic(boosting_predictions_oob, outcome, .pred)

Description

Uses a bootstrapped Isotonic regression model to calibrate probabilities

Usage

cal_estimate_isotonic_boot(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  times = 10,
  parameters = NULL,
  ...
)

## S3 method for class 'data.frame'
cal_estimate_isotonic_boot(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  times = 10,
  parameters = NULL,
  ...,
  .by = NULL
)

## S3 method for class 'tune_results'
cal_estimate_isotonic_boot(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  times = 10,
  parameters = NULL,
  ...
)

## S3 method for class 'grouped_df'
cal_estimate_isotonic_boot(
  .data,
  truth = NULL,
  estimate = NULL,
  times = 10,
  parameters = NULL,
  ...
)

Arguments

Details

This function uses stats::isoreg() to create obtain the calibration values. It runs stats::isoreg() multiple times, and each time with a different seed. The results are saved inside the returned cal_object.

Multiclass Extension

This method is designed to work with two classes. For multiclass, it creates a set of "one versus all" calibrations for each class. After they are applied to the data, the probability estimates are re-normalized to add to one. This final step might compromise the calibration.

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_validate_isotonic_boot()

Examples

# It will automatically identify the probability columns
# if passed a model fitted with tidymodels
cal_estimate_isotonic_boot(segment_logistic, Class)
# Specify the variable names in a vector of unquoted names
cal_estimate_isotonic_boot(segment_logistic, Class, c(.pred_poor, .pred_good))
# dplyr selector functions are also supported
cal_estimate_isotonic_boot(segment_logistic, Class, dplyr::starts_with(".pred"))

Description

Uses a linear regression model to calibrate numeric predictions

Usage

cal_estimate_linear(
  .data,
  truth = NULL,
  estimate = dplyr::matches("^.pred$"),
  smooth = TRUE,
  parameters = NULL,
  ...,
  .by = NULL
)

## S3 method for class 'data.frame'
cal_estimate_linear(
  .data,
  truth = NULL,
  estimate = dplyr::matches("^.pred$"),
  smooth = TRUE,
  parameters = NULL,
  ...,
  .by = NULL
)

## S3 method for class 'tune_results'
cal_estimate_linear(
  .data,
  truth = NULL,
  estimate = dplyr::matches("^.pred$"),
  smooth = TRUE,
  parameters = NULL,
  ...
)

## S3 method for class 'grouped_df'
cal_estimate_linear(
  .data,
  truth = NULL,
  estimate = NULL,
  smooth = TRUE,
  parameters = NULL,
  ...
)

Arguments

Details

This function uses existing modeling functions from other packages to create the calibration:

• stats::glm() is used when smooth is set to FALSE

• mgcv::gam() is used when smooth is set to TRUE

These methods estimate the relationship in the unmodified predicted values and then remove that trend when cal_apply() is invoked.

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_validate_linear()

Examples

library(dplyr)
library(ggplot2)

head(boosting_predictions_test)

# ------------------------------------------------------------------------------
# Before calibration

y_rng <- extendrange(boosting_predictions_test$outcome)

boosting_predictions_test %>%
  ggplot(aes(outcome, .pred)) +
  geom_abline(lty = 2) +
  geom_point(alpha = 1 / 2) +
  geom_smooth(se = FALSE, col = "blue", linewidth = 1.2, alpha = 3 / 4) +
  coord_equal(xlim = y_rng, ylim = y_rng) +
  ggtitle("Before calibration")

# ------------------------------------------------------------------------------
# Smoothed trend removal

smoothed_cal <-
  boosting_predictions_oob %>%
  # It will automatically identify the predicted value columns when the
  # standard tidymodels naming conventions are used.
  cal_estimate_linear(outcome)
smoothed_cal

boosting_predictions_test %>%
  cal_apply(smoothed_cal) %>%
  ggplot(aes(outcome, .pred)) +
  geom_abline(lty = 2) +
  geom_point(alpha = 1 / 2) +
  geom_smooth(se = FALSE, col = "blue", linewidth = 1.2, alpha = 3 / 4) +
  coord_equal(xlim = y_rng, ylim = y_rng) +
  ggtitle("After calibration")

Description

Uses a logistic regression model to calibrate probabilities

Usage

cal_estimate_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  smooth = TRUE,
  parameters = NULL,
  ...
)

## S3 method for class 'data.frame'
cal_estimate_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  smooth = TRUE,
  parameters = NULL,
  ...,
  .by = NULL
)

## S3 method for class 'tune_results'
cal_estimate_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  smooth = TRUE,
  parameters = NULL,
  ...
)

## S3 method for class 'grouped_df'
cal_estimate_logistic(
  .data,
  truth = NULL,
  estimate = NULL,
  smooth = TRUE,
  parameters = NULL,
  ...
)

Arguments

Details

This function uses existing modeling functions from other packages to create the calibration:

• stats::glm() is used when smooth is set to FALSE

• mgcv::gam() is used when smooth is set to TRUE

Multiclass Extension

This method has not been extended to multiclass outcomes. However, the natural multiclass extension is cal_estimate_multinomial().

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_validate_logistic()

Examples

# It will automatically identify the probability columns
# if passed a model fitted with tidymodels
cal_estimate_logistic(segment_logistic, Class)

# Specify the variable names in a vector of unquoted names
cal_estimate_logistic(segment_logistic, Class, c(.pred_poor, .pred_good))

# dplyr selector functions are also supported
cal_estimate_logistic(segment_logistic, Class, dplyr::starts_with(".pred_"))

Description

Uses a Multinomial calibration model to calculate new probabilities

Usage

cal_estimate_multinomial(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  smooth = TRUE,
  parameters = NULL,
  ...
)

## S3 method for class 'data.frame'
cal_estimate_multinomial(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  smooth = TRUE,
  parameters = NULL,
  ...,
  .by = NULL
)

## S3 method for class 'tune_results'
cal_estimate_multinomial(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  smooth = TRUE,
  parameters = NULL,
  ...
)

## S3 method for class 'grouped_df'
cal_estimate_multinomial(
  .data,
  truth = NULL,
  estimate = NULL,
  smooth = TRUE,
  parameters = NULL,
  ...
)

Arguments

Details

When smooth = FALSE, nnet::multinom() function is used to estimate the model, otherwise mgcv::gam() is used.

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_validate_multinomial()

Examples

library(modeldata)
library(parsnip)
library(dplyr)

f <-
  list(
    ~ -0.5 + 0.6 * abs(A),
    ~ ifelse(A > 0 & B > 0, 1.0 + 0.2 * A / B, -2),
    ~ -0.6 * A + 0.50 * B - A * B
  )

set.seed(1)
tr_dat <- sim_multinomial(500, eqn_1 = f[[1]], eqn_2 = f[[2]], eqn_3 = f[[3]])
cal_dat <- sim_multinomial(500, eqn_1 = f[[1]], eqn_2 = f[[2]], eqn_3 = f[[3]])
te_dat <- sim_multinomial(500, eqn_1 = f[[1]], eqn_2 = f[[2]], eqn_3 = f[[3]])

set.seed(2)
rf_fit <-
  rand_forest() %>%
  set_mode("classification") %>%
  set_engine("randomForest") %>%
  fit(class ~ ., data = tr_dat)

cal_pred <-
  predict(rf_fit, cal_dat, type = "prob") %>%
  bind_cols(cal_dat)
te_pred <-
  predict(rf_fit, te_dat, type = "prob") %>%
  bind_cols(te_dat)

cal_plot_windowed(cal_pred, truth = class, window_size = 0.1, step_size = 0.03)

smoothed_mn <- cal_estimate_multinomial(cal_pred, truth = class)

new_test_pred <- cal_apply(te_pred, smoothed_mn)

cal_plot_windowed(new_test_pred, truth = class, window_size = 0.1, step_size = 0.03)

Description

A plot is created to assess whether the observed rate of the event is about the same as the predicted probability of the event from some model.

A sequence of even, mutually exclusive bins are created from zero to one. For each bin, the data whose predicted probability falls within the range of the bin is used to calculate the observed event rate (along with confidence intervals for the event rate). If the predictions are well calibrated, the fitted curve should align with the diagonal line.

Usage

cal_plot_breaks(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  num_breaks = 10,
  conf_level = 0.9,
  include_ribbon = TRUE,
  include_rug = TRUE,
  include_points = TRUE,
  event_level = c("auto", "first", "second"),
  ...
)

## S3 method for class 'data.frame'
cal_plot_breaks(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  num_breaks = 10,
  conf_level = 0.9,
  include_ribbon = TRUE,
  include_rug = TRUE,
  include_points = TRUE,
  event_level = c("auto", "first", "second"),
  ...,
  .by = NULL
)

## S3 method for class 'tune_results'
cal_plot_breaks(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  num_breaks = 10,
  conf_level = 0.9,
  include_ribbon = TRUE,
  include_rug = TRUE,
  include_points = TRUE,
  event_level = c("auto", "first", "second"),
  ...
)

## S3 method for class 'grouped_df'
cal_plot_breaks(
  .data,
  truth = NULL,
  estimate = NULL,
  num_breaks = 10,
  conf_level = 0.9,
  include_ribbon = TRUE,
  include_rug = TRUE,
  include_points = TRUE,
  event_level = c("auto", "first", "second"),
  ...
)

Arguments

Value

A ggplot object.

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_plot_windowed(), cal_plot_logistic()

cal_plot_logistic(), cal_plot_windowed()

Examples

library(ggplot2)
library(dplyr)

cal_plot_breaks(
  segment_logistic,
  Class,
  .pred_good
)

cal_plot_logistic(
  segment_logistic,
  Class,
  .pred_good
)

cal_plot_windowed(
  segment_logistic,
  Class,
  .pred_good
)

# The functions support dplyr groups

model <- glm(Class ~ .pred_good, segment_logistic, family = "binomial")

preds <- predict(model, segment_logistic, type = "response")

gl <- segment_logistic %>%
  mutate(.pred_good = 1 - preds, source = "glm")

combined <- bind_rows(mutate(segment_logistic, source = "original"), gl)

combined %>%
  cal_plot_logistic(Class, .pred_good, .by = source)

# The grouping can be faceted in ggplot2
combined %>%
  cal_plot_logistic(Class, .pred_good, .by = source) +
  facet_wrap(~source) +
  theme(legend.position = "")

Description

A logistic regression model is fit where the original outcome data are used as the outcome and the estimated class probabilities for one class are used as the predictor. If smooth = TRUE, a generalized additive model is fit using mgcv::gam() and the default smoothing method. Otherwise, a simple logistic regression is used.

If the predictions are well calibrated, the fitted curve should align with the diagonal line. Confidence intervals for the fitted line are also shown.

Usage

cal_plot_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  conf_level = 0.9,
  smooth = TRUE,
  include_rug = TRUE,
  include_ribbon = TRUE,
  event_level = c("auto", "first", "second"),
  ...
)

## S3 method for class 'data.frame'
cal_plot_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  conf_level = 0.9,
  smooth = TRUE,
  include_rug = TRUE,
  include_ribbon = TRUE,
  event_level = c("auto", "first", "second"),
  ...,
  .by = NULL
)

## S3 method for class 'tune_results'
cal_plot_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  conf_level = 0.9,
  smooth = TRUE,
  include_rug = TRUE,
  include_ribbon = TRUE,
  event_level = c("auto", "first", "second"),
  ...
)

## S3 method for class 'grouped_df'
cal_plot_logistic(
  .data,
  truth = NULL,
  estimate = NULL,
  conf_level = 0.9,
  smooth = TRUE,
  include_rug = TRUE,
  include_ribbon = TRUE,
  event_level = c("auto", "first", "second"),
  ...
)

Arguments

Value

A ggplot object.

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_plot_windowed(), cal_plot_breaks()

cal_plot_breaks(), cal_plot_windowed()

Examples

library(ggplot2)
library(dplyr)

cal_plot_logistic(
  segment_logistic,
  Class,
  .pred_good
)

cal_plot_logistic(
  segment_logistic,
  Class,
  .pred_good,
  smooth = FALSE
)

Description

A scatter plot of the observed and predicted values is computed where the axes are the same. When smooth = TRUE, a generalized additive model fit is shown. If the predictions are well calibrated, the fitted curve should align with the diagonal line.

Usage

cal_plot_regression(.data, truth = NULL, estimate = NULL, smooth = TRUE, ...)

## S3 method for class 'data.frame'
cal_plot_regression(
  .data,
  truth = NULL,
  estimate = NULL,
  smooth = TRUE,
  ...,
  .by = NULL
)

## S3 method for class 'tune_results'
cal_plot_regression(.data, truth = NULL, estimate = NULL, smooth = TRUE, ...)

## S3 method for class 'grouped_df'
cal_plot_regression(.data, truth = NULL, estimate = NULL, smooth = TRUE, ...)

Arguments

Value

A ggplot object.

Examples

cal_plot_regression(boosting_predictions_oob, outcome, .pred)

cal_plot_regression(boosting_predictions_oob, outcome, .pred,
  alpha = 1 / 6, cex = 3, smooth = FALSE
)

cal_plot_regression(boosting_predictions_oob, outcome, .pred,
  .by = id,
  alpha = 1 / 6, cex = 3, smooth = FALSE
)

Description

A plot is created to assess whether the observed rate of the event is about the sample as the predicted probability of the event from some model. This is similar to cal_plot_breaks(), except that the bins are overlapping.

A sequence of bins are created from zero to one. For each bin, the data whose predicted probability falls within the range of the bin is used to calculate the observed event rate (along with confidence intervals for the event rate).

If the predictions are well calibrated, the fitted curve should align with the diagonal line.

Usage

cal_plot_windowed(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  window_size = 0.1,
  step_size = window_size/2,
  conf_level = 0.9,
  include_ribbon = TRUE,
  include_rug = TRUE,
  include_points = TRUE,
  event_level = c("auto", "first", "second"),
  ...
)

## S3 method for class 'data.frame'
cal_plot_windowed(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  window_size = 0.1,
  step_size = window_size/2,
  conf_level = 0.9,
  include_ribbon = TRUE,
  include_rug = TRUE,
  include_points = TRUE,
  event_level = c("auto", "first", "second"),
  ...,
  .by = NULL
)

## S3 method for class 'tune_results'
cal_plot_windowed(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  window_size = 0.1,
  step_size = window_size/2,
  conf_level = 0.9,
  include_ribbon = TRUE,
  include_rug = TRUE,
  include_points = TRUE,
  event_level = c("auto", "first", "second"),
  ...
)

## S3 method for class 'grouped_df'
cal_plot_windowed(
  .data,
  truth = NULL,
  estimate = NULL,
  window_size = 0.1,
  step_size = window_size/2,
  conf_level = 0.9,
  include_ribbon = TRUE,
  include_rug = TRUE,
  include_points = TRUE,
  event_level = c("auto", "first", "second"),
  ...
)

Arguments

Value

A ggplot object.

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_plot_logistic(), cal_plot_breaks()

cal_plot_breaks(), cal_plot_logistic()

Examples

library(ggplot2)
library(dplyr)

cal_plot_windowed(
  segment_logistic,
  Class,
  .pred_good
)

# More breaks
cal_plot_windowed(
  segment_logistic,
  Class,
  .pred_good,
  window_size = 0.05
)

Description

This function uses resampling to measure the effect of calibrating predicted values.

Usage

cal_validate_beta(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'resample_results'
cal_validate_beta(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'rset'
cal_validate_beta(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'tune_results'
cal_validate_beta(
  .data,
  truth = NULL,
  estimate = NULL,
  metrics = NULL,
  save_pred = FALSE,
  ...
)

Arguments

Details

These functions are designed to calculate performance with and without calibration. They use resampling to measure out-of-sample effectiveness. There are two ways to pass the data in:

• If you have a data frame of predictions, an rset object can be created via rsample functions. See the example below.

• If you have already made a resampling object from the original data and used it with tune::fit_resamples(), you can pass that object to the calibration function and it will use the same resampling scheme. If a different resampling scheme should be used, run tune::collect_predictions() on the object and use the process in the previous bullet point.

Please note that these functions do not apply to tune_result objects. The notion of "validation" implies that the tuning parameter selection has been resolved.

collect_predictions() can be used to aggregate the metrics for analysis.

Value

The original object with a .metrics_cal column and, optionally, an additional .predictions_cal column. The class cal_rset is also added.

Performance Metrics

By default, the average of the Brier scores is returned. Any appropriate yardstick::metric_set() can be used. The validation function compares the average of the metrics before, and after the calibration.

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_estimate_beta()

Examples

library(dplyr)

if (rlang::is_installed("betacal")) {
  segment_logistic %>%
    rsample::vfold_cv() %>%
    cal_validate_beta(Class)
}

Description

This function uses resampling to measure the effect of calibrating predicted values.

Usage

cal_validate_isotonic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'resample_results'
cal_validate_isotonic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'rset'
cal_validate_isotonic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'tune_results'
cal_validate_isotonic(
  .data,
  truth = NULL,
  estimate = NULL,
  metrics = NULL,
  save_pred = FALSE,
  ...
)

Arguments

Details

These functions are designed to calculate performance with and without calibration. They use resampling to measure out-of-sample effectiveness. There are two ways to pass the data in:

• If you have a data frame of predictions, an rset object can be created via rsample functions. See the example below.

• If you have already made a resampling object from the original data and used it with tune::fit_resamples(), you can pass that object to the calibration function and it will use the same resampling scheme. If a different resampling scheme should be used, run tune::collect_predictions() on the object and use the process in the previous bullet point.

Please note that these functions do not apply to tune_result objects. The notion of "validation" implies that the tuning parameter selection has been resolved.

collect_predictions() can be used to aggregate the metrics for analysis.

Value

The original object with a .metrics_cal column and, optionally, an additional .predictions_cal column. The class cal_rset is also added.

Performance Metrics

By default, the average of the Brier scores (classification calibration) or the root mean squared error (regression) is returned. Any appropriate yardstick::metric_set() can be used. The validation function compares the average of the metrics before, and after the calibration.

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_estimate_isotonic()

Examples

library(dplyr)

segment_logistic %>%
  rsample::vfold_cv() %>%
  cal_validate_isotonic(Class)

Description

This function uses resampling to measure the effect of calibrating predicted values.

Usage

cal_validate_isotonic_boot(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'resample_results'
cal_validate_isotonic_boot(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'rset'
cal_validate_isotonic_boot(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'tune_results'
cal_validate_isotonic_boot(
  .data,
  truth = NULL,
  estimate = NULL,
  metrics = NULL,
  save_pred = FALSE,
  ...
)

Arguments

Details

These functions are designed to calculate performance with and without calibration. They use resampling to measure out-of-sample effectiveness. There are two ways to pass the data in:

• If you have a data frame of predictions, an rset object can be created via rsample functions. See the example below.

• If you have already made a resampling object from the original data and used it with tune::fit_resamples(), you can pass that object to the calibration function and it will use the same resampling scheme. If a different resampling scheme should be used, run tune::collect_predictions() on the object and use the process in the previous bullet point.

Please note that these functions do not apply to tune_result objects. The notion of "validation" implies that the tuning parameter selection has been resolved.

collect_predictions() can be used to aggregate the metrics for analysis.

Value

The original object with a .metrics_cal column and, optionally, an additional .predictions_cal column. The class cal_rset is also added.

Performance Metrics

By default, the average of the Brier scores (classification calibration) or the root mean squared error (regression) is returned. Any appropriate yardstick::metric_set() can be used. The validation function compares the average of the metrics before, and after the calibration.

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_estimate_isotonic_boot()

Examples

library(dplyr)

segment_logistic %>%
  rsample::vfold_cv() %>%
  cal_validate_isotonic_boot(Class)

Description

Measure performance with and without using linear regression calibration

Usage

cal_validate_linear(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'resample_results'
cal_validate_linear(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'rset'
cal_validate_linear(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

Arguments

Performance Metrics

By default, the average of the root mean square error (RMSE) is returned. Any appropriate yardstick::metric_set() can be used. The validation function compares the average of the metrics before, and after the calibration.

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_estimate_linear()

Examples

library(dplyr)
library(yardstick)
library(rsample)

head(boosting_predictions_test)

reg_stats <- metric_set(rmse, ccc)

set.seed(828)
boosting_predictions_oob %>%
  # Resample with 10-fold cross-validation
  vfold_cv() %>%
  cal_validate_linear(truth = outcome, smooth = FALSE, metrics = reg_stats)

Description

This function uses resampling to measure the effect of calibrating predicted values.

Usage

cal_validate_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'resample_results'
cal_validate_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'rset'
cal_validate_logistic(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'tune_results'
cal_validate_logistic(
  .data,
  truth = NULL,
  estimate = NULL,
  metrics = NULL,
  save_pred = FALSE,
  ...
)

Arguments

Details

These functions are designed to calculate performance with and without calibration. They use resampling to measure out-of-sample effectiveness. There are two ways to pass the data in:

• If you have a data frame of predictions, an rset object can be created via rsample functions. See the example below.

• If you have already made a resampling object from the original data and used it with tune::fit_resamples(), you can pass that object to the calibration function and it will use the same resampling scheme. If a different resampling scheme should be used, run tune::collect_predictions() on the object and use the process in the previous bullet point.

Please note that these functions do not apply to tune_result objects. The notion of "validation" implies that the tuning parameter selection has been resolved.

collect_predictions() can be used to aggregate the metrics for analysis.

Value

The original object with a .metrics_cal column and, optionally, an additional .predictions_cal column. The class cal_rset is also added.

Performance Metrics

By default, the average of the Brier scores is returned. Any appropriate yardstick::metric_set() can be used. The validation function compares the average of the metrics before, and after the calibration.

See Also

https://www.tidymodels.org/learn/models/calibration/, cal_estimate_logistic()

Examples

library(dplyr)

# ---------------------------------------------------------------------------
# classification example

segment_logistic %>%
  rsample::vfold_cv() %>%
  cal_validate_logistic(Class)

Description

This function uses resampling to measure the effect of calibrating predicted values.

Usage

cal_validate_multinomial(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'resample_results'
cal_validate_multinomial(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'rset'
cal_validate_multinomial(
  .data,
  truth = NULL,
  estimate = dplyr::starts_with(".pred_"),
  metrics = NULL,
  save_pred = FALSE,
  ...
)

## S3 method for class 'tune_results'
cal_validate_multinomial(
  .data,
  truth = NULL,
  estimate = NULL,
  metrics = NULL,
  save_pred = FALSE,
  ...
)

Arguments

Details

These functions are designed to calculate performance with and without calibration. They use resampling to measure out-of-sample effectiveness. There are two ways to pass the data in:

• If you have a data frame of predictions, an rset object can be created via rsample functions. See the example below.

• If you have already made a resampling object from the original data and used it with tune::fit_resamples(), you can pass that object to the calibration function and it will use the same resampling scheme. If a different resampling scheme should be used, run tune::collect_predictions() on the object and use the process in the previous bullet point.

Please note that these functions do not apply to tune_result objects. The notion of "validation" implies that the tuning parameter selection has been resolved.

collect_predictions() can be used to aggregate the metrics for analysis.

Value

The original object with a .metrics_cal column and, optionally, an additional .predictions_cal column. The class cal_rset is also added.

Performance Metrics

By default, the average of the Brier scores is returned. Any appropriate yardstick::metric_set() can be used. The validation function compares the average of the metrics before, and after the calibration.

See Also

cal_apply(), cal_estimate_multinomial()

Examples

library(dplyr)

species_probs %>%
  rsample::vfold_cv() %>%
  cal_validate_multinomial(Species)

Description

class_pred() creates a class_pred object from a factor or ordered factor. You can optionally specify values of the factor to be set as equivocal.

Usage

class_pred(x = factor(), which = integer(), equivocal = "[EQ]")

Arguments

Details

Equivocal values are those that you feel unsure about, and would like to exclude from performance calculations or other metrics.

Examples

x <- factor(c("Yes", "No", "Yes", "Yes"))

# Create a class_pred object from a factor
class_pred(x)

# Say you aren't sure about that 2nd "Yes" value. You could mark it as
# equivocal.
class_pred(x, which = 3)

# Maybe you want a different equivocal label
class_pred(x, which = 3, equivocal = "eq_value")

Description

Obtain and format metrics produced by calibration validation

Usage

## S3 method for class 'cal_rset'
collect_metrics(x, summarize = TRUE, ...)

Arguments

Value

A tibble

Description

Obtain and format predictions produced by calibration validation

Usage

## S3 method for class 'cal_rset'
collect_predictions(x, summarize = TRUE, ...)

Arguments

Value

A tibble

Description

Controlling the numeric details for conformal inference

Usage

control_conformal_full(
  method = "iterative",
  trial_points = 100,
  var_multiplier = 10,
  max_iter = 100,
  tolerance = .Machine$double.eps^0.25,
  progress = FALSE,
  required_pkgs = character(0),
  seed = sample.int(10^5, 1)
)

Arguments

Value

A list object with the options given by the user.

Description

Nonparametric prediction intervals can be computed for fitted regression workflow objects using the CV+ conformal inference method described by Barber at al (2018).

Usage

int_conformal_cv(object, ...)

## Default S3 method:
int_conformal_cv(object, ...)

## S3 method for class 'resample_results'
int_conformal_cv(object, ...)

## S3 method for class 'tune_results'
int_conformal_cv(object, parameters, ...)

Arguments

Details

This function implements the CV+ method found in Section 3 of Barber at al (2018). It uses the resampled model fits and their associated holdout residuals to make prediction intervals for regression models.

This function prepares the objects for the computations. The predict() method computes the intervals for new data.

This method was developed for V-fold cross-validation (no repeats). Interval coverage is unknown for any other resampling methods. The function will not stop the computations for other types of resamples, but we have no way of knowing whether the results are appropriate.

Value

An object of class "int_conformal_cv" containing the information to create intervals. The predict() method is used to produce the intervals.

References

Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas, Ryan J. Tibshirani "Predictive inference with the jackknife+," The Annals of Statistics, 49(1), 486-507, 2021

See Also

predict.int_conformal_cv()

Examples

library(workflows)
library(dplyr)
library(parsnip)
library(rsample)
library(tune)
library(modeldata)

set.seed(2)
sim_train <- sim_regression(200)
sim_new <- sim_regression(5) %>% select(-outcome)

sim_rs <- vfold_cv(sim_train)

# We'll use a neural network model
mlp_spec <-
  mlp(hidden_units = 5, penalty = 0.01) %>%
  set_mode("regression")

# Use a control function that saves the predictions as well as the models.
# Consider using the butcher package in the extracts function to have smaller
# object sizes

ctrl <- control_resamples(save_pred = TRUE, extract = I)

set.seed(3)
nnet_res <-
  mlp_spec %>%
  fit_resamples(outcome ~ ., resamples = sim_rs, control = ctrl)

nnet_int_obj <- int_conformal_cv(nnet_res)
nnet_int_obj

predict(nnet_int_obj, sim_new)

Description

Nonparametric prediction intervals can be computed for fitted workflow objects using the conformal inference method described by Lei at al (2018).

Usage

int_conformal_full(object, ...)

## Default S3 method:
int_conformal_full(object, ...)

## S3 method for class 'workflow'
int_conformal_full(object, train_data, ..., control = control_conformal_full())

Arguments

Details

This function implements what is usually called "full conformal inference" (see Algorithm 1 in Lei et al (2018)) since it uses the entire training set to compute the intervals.

This function prepares the objects for the computations. The predict() method computes the intervals for new data.

For a given new_data observation, the predictors are appended to the original training set. Then, different "trial" values of the outcome are substituted in for that observation's outcome and the model is re-fit. From each model, the residual associated with the trial value is compared to a quantile of the distribution of the other residuals. Usually the absolute values of the residuals are used. Once the residual of a trial value exceeds the distributional quantile, the value is one of the bounds.

The literature proposed using a grid search of trial values to find the two points that correspond to the prediction intervals. To use this approach, set method = "grid" in control_conformal_full(). However, the default method ⁠"search⁠ uses two different one-dimensional iterative searches on either side of the predicted value to find values that correspond to the prediction intervals.

For medium to large data sets, the iterative search method is likely to generate slightly smaller intervals. For small training sets, grid search is more likely to have somewhat smaller intervals (and will be more stable). Otherwise, the iterative search method is more precise and several folds faster.

To determine a range of possible values of the intervals, used by both methods, the initial set of training set residuals are modeled using a Gamma generalized linear model with a log link (see the reference by Aitkin below). For a new sample, the absolute size of the residual is estimated and a multiple of this value is computed as an initial guess of the search boundaries.

Speed

The time it takes to compute the intervals depends on the training set size, search parameters (i.e., convergence criterion, number of iterations), the grid size, and the number of worker processes that are used. For the last item, the computations can be parallelized using the future and furrr packages.

To use parallelism, the future::plan() function can be invoked to create a parallel backend. For example, let’s make an initial workflow:

library(tidymodels)
library(probably)
library(future)

tidymodels_prefer()

## Make a fitted workflow from some simulated data:
set.seed(121)
train_dat <- sim_regression(200)
new_dat   <- sim_regression(  5) %>% select(-outcome)

lm_fit <- 
  workflow() %>% 
  add_model(linear_reg()) %>% 
  add_formula(outcome ~ .) %>% 
  fit(data = train_dat)

# Create the object to be used to make prediction intervals
lm_conform <- int_conformal_full(lm_fit, train_dat)

We’ll use a "multisession" parallel processing plan to compute the intervals for the five new samples in parallel:

plan("multisession")

# This is run in parallel:
predict(lm_conform, new_dat)

## # A tibble: 5 x 2
##   .pred_lower .pred_upper
##         <dbl>       <dbl>
## 1       -17.9        59.6
## 2       -33.7        51.1
## 3       -30.6        48.2
## 4       -17.3        59.6
## 5       -23.3        55.2

Using simulations, there are slightly sub-linear speed-ups when using parallel processing to compute the row-wise intervals.

In comparison with parametric intervals:

predict(lm_fit, new_dat, type = "pred_int")

## # A tibble: 5 x 2
##   .pred_lower .pred_upper
##         <dbl>       <dbl>
## 1       -19.2        59.1
## 2       -31.8        49.7
## 3       -31.0        47.6
## 4       -17.8        60.1
## 5       -23.6        54.3

Value

An object of class "int_conformal_full" containing the information to create intervals (which includes the training set data). The predict() method is used to produce the intervals.

References

Jing Lei, Max G'Sell, Alessandro Rinaldo, Ryan J. Tibshirani and Larry Wasserman (2018) Distribution-Free Predictive Inference for Regression, Journal of the American Statistical Association, 113:523, 1094-1111

Murray Aitkin, Modelling Variance Heterogeneity in Normal Regression Using GLIM, Journal of the Royal Statistical Society Series C: Applied Statistics, Volume 36, Issue 3, November 1987, Pages 332–339.

See Also

predict.int_conformal_full()

Description

Nonparametric prediction intervals can be computed for fitted regression workflow objects using the split conformal inference method described by Romano et al (2019). To compute quantiles, this function uses Quantile Random Forests instead of classic quantile regression.

Usage

int_conformal_quantile(object, ...)

## S3 method for class 'workflow'
int_conformal_quantile(object, train_data, cal_data, level = 0.95, ...)

Arguments

Details

Note that the significance level should be specified in this function (instead of the predict() method).

cal_data should be large enough to get a good estimates of a extreme quantile (e.g., the 95th for 95% interval) and should not include rows that were in the original training set.

Note that the because of the method used to construct the interval, it is possible that the prediction intervals will not include the predicted value.

Value

An object of class "int_conformal_quantile" containing the information to create intervals (which includes object). The predict() method is used to produce the intervals.

References

Romano, Yaniv, Evan Patterson, and Emmanuel Candes. "Conformalized quantile regression." Advances in neural information processing systems 32 (2019).

See Also

predict.int_conformal_quantile()

Examples

library(workflows)
library(dplyr)
library(parsnip)
library(rsample)
library(tune)
library(modeldata)

set.seed(2)
sim_train <- sim_regression(500)
sim_cal <- sim_regression(200)
sim_new <- sim_regression(5) %>% select(-outcome)

# We'll use a neural network model
mlp_spec <-
  mlp(hidden_units = 5, penalty = 0.01) %>%
  set_mode("regression")

mlp_wflow <-
  workflow() %>%
  add_model(mlp_spec) %>%
  add_formula(outcome ~ .)

mlp_fit <- fit(mlp_wflow, data = sim_train)

mlp_int <- int_conformal_quantile(mlp_fit, sim_train, sim_cal,
  level = 0.90
)
mlp_int

predict(mlp_int, sim_new)

Description

Nonparametric prediction intervals can be computed for fitted regression workflow objects using the split conformal inference method described by Lei et al (2018).

Usage

int_conformal_split(object, ...)

## Default S3 method:
int_conformal_split(object, ...)

## S3 method for class 'workflow'
int_conformal_split(object, cal_data, ...)

Arguments

Details

This function implements what is usually called "split conformal inference" (see Algorithm 1 in Lei et al (2018)).

This function prepares the statistics for the interval computations. The predict() method computes the intervals for new data and the signficance level is specified there.

cal_data should be large enough to get a good estimates of a extreme quantile (e.g., the 95th for 95% interval) and should not include rows that were in the original training set.

Value

An object of class "int_conformal_split" containing the information to create intervals (which includes object). The predict() method is used to produce the intervals.

References

Lei, Jing, et al. "Distribution-free predictive inference for regression." Journal of the American Statistical Association 113.523 (2018): 1094-1111.

See Also

predict.int_conformal_split()

Examples

library(workflows)
library(dplyr)
library(parsnip)
library(rsample)
library(tune)
library(modeldata)

set.seed(2)
sim_train <- sim_regression(500)
sim_cal <- sim_regression(200)
sim_new <- sim_regression(5) %>% select(-outcome)

# We'll use a neural network model
mlp_spec <-
  mlp(hidden_units = 5, penalty = 0.01) %>%
  set_mode("regression")

mlp_wflow <-
  workflow() %>%
  add_model(mlp_spec) %>%
  add_formula(outcome ~ .)

mlp_fit <- fit(mlp_wflow, data = sim_train)

mlp_int <- int_conformal_split(mlp_fit, sim_cal)
mlp_int

predict(mlp_int, sim_new, level = 0.90)

Description

is_class_pred() checks if an object is a class_pred object.

Usage

is_class_pred(x)

Arguments

Examples

x <- class_pred(factor(1:5))

is_class_pred(x)

Description

The levels of a class_pred object do not include the equivocal value.

Usage

## S3 method for class 'class_pred'
levels(x)

Arguments

Examples

x <- class_pred(factor(1:5), which = 1)

# notice that even though `1` is not in the `class_pred` vector, the
# level remains from the original factor
levels(x)

Description

These functions provide multiple methods of checking for equivocal values, and finding their locations.

Usage

is_equivocal(x)

which_equivocal(x)

any_equivocal(x)

Arguments

Value

is_equivocal() returns a logical vector the same length as x where TRUE means the value is equivocal.

which_equivocal() returns an integer vector specifying the locations of the equivocal values.

any_equivocal() returns TRUE if there are any equivocal values.

Examples

x <- class_pred(factor(1:10), which = c(2, 5))

is_equivocal(x)

which_equivocal(x)

any_equivocal(x)

Description

These functions can be used to convert class probability estimates to class_pred objects with an optional equivocal zone.

Usage

make_class_pred(..., levels, ordered = FALSE, min_prob = 1/length(levels))

make_two_class_pred(
  estimate,
  levels,
  threshold = 0.5,
  ordered = FALSE,
  buffer = NULL
)

Arguments

Value

A vector of class class_pred.

Examples

library(dplyr)

good <- segment_logistic$.pred_good
lvls <- levels(segment_logistic$Class)

# Equivocal zone of .5 +/- .15
make_two_class_pred(good, lvls, buffer = 0.15)

# Equivocal zone of c(.5 - .05, .5 + .15)
make_two_class_pred(good, lvls, buffer = c(0.05, 0.15))

# These functions are useful alongside dplyr::mutate()
segment_logistic %>%
  mutate(
    .class_pred = make_two_class_pred(
      estimate = .pred_good,
      levels = levels(Class),
      buffer = 0.15
    )
  )

# Multi-class example
# Note that we provide class probability columns in the same
# order as the levels
species_probs %>%
  mutate(
    .class_pred = make_class_pred(
      .pred_bobcat, .pred_coyote, .pred_gray_fox,
      levels = levels(Species),
      min_prob = .5
    )
  )

Description

Prediction intervals from conformal methods

Usage

## S3 method for class 'int_conformal_full'
predict(object, new_data, level = 0.95, ...)

## S3 method for class 'int_conformal_cv'
predict(object, new_data, level = 0.95, ...)

## S3 method for class 'int_conformal_quantile'
predict(object, new_data, ...)

## S3 method for class 'int_conformal_split'
predict(object, new_data, level = 0.95, ...)

Arguments

Details

For the CV+. estimator produced by int_conformal_cv(), the intervals are centered around the mean of the predictions produced by the resample-specific model. For example, with 10-fold cross-validation, .pred is the average of the predictions from the 10 models produced by each fold. This may differ from the prediction generated from a model fit that was trained on the entire training set, especially if the training sets are small.

Value

A tibble with columns .pred_lower and .pred_upper. If the computations for the prediction bound fail, a missing value is used. For objects produced by int_conformal_cv(), an additional .pred column is also returned (see Details below).

See Also

int_conformal_full(), int_conformal_cv()

Description

The reportable rate is defined as the percentage of class predictions that are not equivocal.

Usage

reportable_rate(x)

Arguments

Details

The reportable rate is calculated as (n_not_equivocal / n).

Examples

x <- class_pred(factor(1:5), which = c(1, 2))

# 3 / 5
reportable_rate(x)

Description

Image segmentation predictions

Details

These objects contain test set predictions for the cell segmentation data from Hill, LaPan, Li and Haney (2007). Each data frame are the results from different models (naive Bayes and logistic regression).

Value

Source

Hill, LaPan, Li and Haney (2007). Impact of image segmentation on high-content screening data quality for SK-BR-3 cells, BMC Bioinformatics, Vol. 8, pg. 340, https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-340.

Examples

data(segment_naive_bayes)
data(segment_logistic)

Description

Predictions on animal species

Details

These data are holdout predictions from resampling for the animal scat data of Reid (2015) based on a C5.0 classification model.

Value

Source

Reid, R. E. B. (2015). A morphometric modeling approach to distinguishing among bobcat, coyote and gray fox scats. Wildlife Biology, 21(5), 254-262

Examples

data(species_probs)
str(species_probs)

Description

threshold_perf() can take a set of class probability predictions and determine performance characteristics across different values of the probability threshold and any existing groups.

Usage

threshold_perf(.data, ...)

## S3 method for class 'data.frame'
threshold_perf(
  .data,
  truth,
  estimate,
  thresholds = NULL,
  metrics = NULL,
  na_rm = TRUE,
  event_level = "first",
  ...
)

Arguments

Details

Note that that the global option yardstick.event_first will be used to determine which level is the event of interest. For more details, see the Relevant level section of yardstick::sens().

The default calculated metrics are:

• yardstick::j_index()

• yardstick::sens()

• yardstick::spec()

• distance = (1 - sens) ^ 2 + (1 - spec) ^ 2

If a custom metric is passed that does not compute sensitivity and specificity, the distance metric is not computed.

Value

A tibble with columns: .threshold, .estimator, .metric, .estimate and any existing groups.

Examples

library(dplyr)
data("segment_logistic")

# Set the threshold to 0.6
# > 0.6 = good
# < 0.6 = poor
threshold_perf(segment_logistic, Class, .pred_good, thresholds = 0.6)

# Set the threshold to multiple values
thresholds <- seq(0.5, 0.9, by = 0.1)

segment_logistic %>%
  threshold_perf(Class, .pred_good, thresholds)

# ---------------------------------------------------------------------------

# It works with grouped data frames as well
# Let's mock some resampled data
resamples <- 5

mock_resamples <- resamples %>%
  replicate(
    expr = sample_n(segment_logistic, 100, replace = TRUE),
    simplify = FALSE
  ) %>%
  bind_rows(.id = "resample")

resampled_threshold_perf <- mock_resamples %>%
  group_by(resample) %>%
  threshold_perf(Class, .pred_good, thresholds)

resampled_threshold_perf

# Average over the resamples
resampled_threshold_perf %>%
  group_by(.metric, .threshold) %>%
  summarise(.estimate = mean(.estimate))