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_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 |
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 |