Tools for Post-Processing Predicted Values

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Documentation for package ‘probably’ version 1.1.0

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A B C I L M P R S T W

-- A --

any_equivocal Locate equivocal values
append_class_pred Add a 'class_pred' column
as_class_pred Coerce to a 'class_pred' object

-- B --

boosting_predictions Boosted regression trees predictions
boosting_predictions_oob Boosted regression trees predictions
boosting_predictions_test Boosted regression trees predictions
bound_prediction Truncate a numeric prediction column

-- C --

cal_apply Applies a calibration to a set of existing predictions
cal_apply.cal_object Applies a calibration to a set of existing predictions
cal_apply.data.frame Applies a calibration to a set of existing predictions
cal_apply.tune_results Applies a calibration to a set of existing predictions
cal_estimate_beta Uses a Beta calibration model to calculate new probabilities
cal_estimate_beta.data.frame Uses a Beta calibration model to calculate new probabilities
cal_estimate_beta.grouped_df Uses a Beta calibration model to calculate new probabilities
cal_estimate_beta.tune_results Uses a Beta calibration model to calculate new probabilities
cal_estimate_isotonic Uses an Isotonic regression model to calibrate model predictions.
cal_estimate_isotonic.data.frame Uses an Isotonic regression model to calibrate model predictions.
cal_estimate_isotonic.grouped_df Uses an Isotonic regression model to calibrate model predictions.
cal_estimate_isotonic.tune_results Uses an Isotonic regression model to calibrate model predictions.
cal_estimate_isotonic_boot Uses a bootstrapped Isotonic regression model to calibrate probabilities
cal_estimate_isotonic_boot.data.frame Uses a bootstrapped Isotonic regression model to calibrate probabilities
cal_estimate_isotonic_boot.grouped_df Uses a bootstrapped Isotonic regression model to calibrate probabilities
cal_estimate_isotonic_boot.tune_results Uses a bootstrapped Isotonic regression model to calibrate probabilities
cal_estimate_linear Uses a linear regression model to calibrate numeric predictions
cal_estimate_linear.data.frame Uses a linear regression model to calibrate numeric predictions
cal_estimate_linear.grouped_df Uses a linear regression model to calibrate numeric predictions
cal_estimate_linear.tune_results Uses a linear regression model to calibrate numeric predictions
cal_estimate_logistic Uses a logistic regression model to calibrate probabilities
cal_estimate_logistic.data.frame Uses a logistic regression model to calibrate probabilities
cal_estimate_logistic.grouped_df Uses a logistic regression model to calibrate probabilities
cal_estimate_logistic.tune_results Uses a logistic regression model to calibrate probabilities
cal_estimate_multinomial Uses a Multinomial calibration model to calculate new probabilities
cal_estimate_multinomial.data.frame Uses a Multinomial calibration model to calculate new probabilities
cal_estimate_multinomial.grouped_df Uses a Multinomial calibration model to calculate new probabilities
cal_estimate_multinomial.tune_results Uses a Multinomial calibration model to calculate new probabilities
cal_estimate_none Do not calibrate model predictions.
cal_estimate_none.data.frame Do not calibrate model predictions.
cal_estimate_none.grouped_df Do not calibrate model predictions.
cal_estimate_none.tune_results Do not calibrate model predictions.
cal_plot_breaks Probability calibration plots via binning
cal_plot_breaks.data.frame Probability calibration plots via binning
cal_plot_breaks.grouped_df Probability calibration plots via binning
cal_plot_breaks.tune_results Probability calibration plots via binning
cal_plot_logistic Probability calibration plots via logistic regression
cal_plot_logistic.data.frame Probability calibration plots via logistic regression
cal_plot_logistic.grouped_df Probability calibration plots via logistic regression
cal_plot_logistic.tune_results Probability calibration plots via logistic regression
cal_plot_regression Regression calibration plots
cal_plot_regression.data.frame Regression calibration plots
cal_plot_regression.grouped_df Regression calibration plots
cal_plot_regression.tune_results Regression calibration plots
cal_plot_windowed Probability calibration plots via moving windows
cal_plot_windowed.data.frame Probability calibration plots via moving windows
cal_plot_windowed.grouped_df Probability calibration plots via moving windows
cal_plot_windowed.tune_results Probability calibration plots via moving windows
cal_validate_beta Measure performance with and without using Beta calibration
cal_validate_beta.resample_results Measure performance with and without using Beta calibration
cal_validate_beta.rset Measure performance with and without using Beta calibration
cal_validate_beta.tune_results Measure performance with and without using Beta calibration
cal_validate_isotonic Measure performance with and without using isotonic regression calibration
cal_validate_isotonic.resample_results Measure performance with and without using isotonic regression calibration
cal_validate_isotonic.rset Measure performance with and without using isotonic regression calibration
cal_validate_isotonic.tune_results Measure performance with and without using isotonic regression calibration
cal_validate_isotonic_boot Measure performance with and without using bagged isotonic regression calibration
cal_validate_isotonic_boot.resample_results Measure performance with and without using bagged isotonic regression calibration
cal_validate_isotonic_boot.rset Measure performance with and without using bagged isotonic regression calibration
cal_validate_isotonic_boot.tune_results Measure performance with and without using bagged isotonic regression calibration
cal_validate_linear Measure performance with and without using linear regression calibration
cal_validate_linear.resample_results Measure performance with and without using linear regression calibration
cal_validate_linear.rset Measure performance with and without using linear regression calibration
cal_validate_logistic Measure performance with and without using logistic calibration
cal_validate_logistic.resample_results Measure performance with and without using logistic calibration
cal_validate_logistic.rset Measure performance with and without using logistic calibration
cal_validate_logistic.tune_results Measure performance with and without using logistic calibration
cal_validate_multinomial Measure performance with and without using multinomial calibration
cal_validate_multinomial.resample_results Measure performance with and without using multinomial calibration
cal_validate_multinomial.rset Measure performance with and without using multinomial calibration
cal_validate_multinomial.tune_results Measure performance with and without using multinomial calibration
cal_validate_none Measure performance without using calibration
cal_validate_none.resample_results Measure performance without using calibration
cal_validate_none.rset Measure performance without using calibration
cal_validate_none.tune_results Measure performance without using calibration
class_pred Create a class prediction object
collect_metrics.cal_rset Obtain and format metrics produced by calibration validation
collect_predictions.cal_rset Obtain and format predictions produced by calibration validation
control_conformal_full Controlling the numeric details for conformal inference

-- I --

int_conformal_cv Prediction intervals via conformal inference CV+
int_conformal_cv.default Prediction intervals via conformal inference CV+
int_conformal_cv.resample_results Prediction intervals via conformal inference CV+
int_conformal_cv.tune_results Prediction intervals via conformal inference CV+
int_conformal_full Prediction intervals via conformal inference
int_conformal_full.default Prediction intervals via conformal inference
int_conformal_full.workflow Prediction intervals via conformal inference
int_conformal_quantile Prediction intervals via conformal inference and quantile regression
int_conformal_quantile.workflow Prediction intervals via conformal inference and quantile regression
int_conformal_split Prediction intervals via split conformal inference
int_conformal_split.default Prediction intervals via split conformal inference
int_conformal_split.workflow Prediction intervals via split conformal inference
is_class_pred Test if an object inherits from 'class_pred'
is_equivocal Locate equivocal values

-- L --

levels.class_pred Extract 'class_pred' levels
locate-equivocal Locate equivocal values

-- M --

make_class_pred Create a 'class_pred' vector from class probabilities
make_two_class_pred Create a 'class_pred' vector from class probabilities

-- P --

predict.int_conformal_cv Prediction intervals from conformal methods
predict.int_conformal_full Prediction intervals from conformal methods
predict.int_conformal_quantile Prediction intervals from conformal methods
predict.int_conformal_split Prediction intervals from conformal methods

-- R --

reportable_rate Calculate the reportable rate

-- S --

segment_logistic Image segmentation predictions
segment_naive_bayes Image segmentation predictions
species_probs Predictions on animal species

-- T --

threshold_perf Generate performance metrics across probability thresholds
threshold_perf.data.frame Generate performance metrics across probability thresholds

-- W --

which_equivocal Locate equivocal values