Nothing
Code
print(sl_logistic)
Message
-- Probability Calibration
Method: Logistic regression
Type: Binary
Source class: Data Frame
Data points: 1,010
Truth variable: `Class`
Estimate variables:
`.pred_good` ==> good
`.pred_poor` ==> poor
We can't connect the specified prediction columns to some factor levels (good). The selected columns were .pred_poor. Are there more columns to add in the function call?
The number of outcome factor levels isn't consistent with the calibration method. Only two class `truth` factors are allowed. The given levels were: 'VF', 'F', 'M', 'L'
Code
print(sl_logistic_group)
Message
-- Probability Calibration
Method: Logistic regression
Type: Binary
Source class: Data Frame
Data points: 1,010, split in 2 groups
Truth variable: `Class`
Estimate variables:
`.pred_good` ==> good
`.pred_poor` ==> poor
x `.by` cannot select more than one column.
i The following 2 columns were selected:
i group1 and group2
Code
print(tl_logistic)
Message
-- Probability Calibration
Method: Logistic regression
Type: Binary
Source class: Tune Results
Data points: 4,000, split in 8 groups
Truth variable: `class`
Estimate variables:
`.pred_class_1` ==> class_1
`.pred_class_2` ==> class_2
The number of outcome factor levels isn't consistent with the calibration method. Only two class `truth` factors are allowed. The given levels were: '.pred_one', '.pred_two', '.pred_three'
x This function does not work with grouped data frames.
i Apply `dplyr::ungroup()` and use the `.by` argument.
Code
print(sl_gam)
Message
-- Probability Calibration
Method: Generalized additive model
Type: Binary
Source class: Data Frame
Data points: 1,010
Truth variable: `Class`
Estimate variables:
`.pred_good` ==> good
`.pred_poor` ==> poor
Code
print(sl_gam_group)
Message
-- Probability Calibration
Method: Generalized additive model
Type: Binary
Source class: Data Frame
Data points: 1,010, split in 2 groups
Truth variable: `Class`
Estimate variables:
`.pred_good` ==> good
`.pred_poor` ==> poor
x `.by` cannot select more than one column.
i The following 2 columns were selected:
i group1 and group2
Code
print(tl_gam)
Message
-- Probability Calibration
Method: Generalized additive model
Type: Binary
Source class: Tune Results
Data points: 4,000, split in 8 groups
Truth variable: `class`
Estimate variables:
`.pred_class_1` ==> class_1
`.pred_class_2` ==> class_2
Code
print(sl_isotonic)
Message
-- Probability Calibration
Method: Isotonic regression
Type: Binary
Source class: Data Frame
Data points: 1,010
Unique Predicted Values: 66
Truth variable: `Class`
Estimate variables:
`.pred_good` ==> good
`.pred_poor` ==> poor
Code
print(sl_isotonic_group)
Message
-- Probability Calibration
Method: Isotonic regression
Type: Binary
Source class: Data Frame
Data points: 1,010, split in 2 groups
Unique Predicted Values: 19
Truth variable: `Class`
Estimate variables:
`.pred_good` ==> good
`.pred_poor` ==> poor
x `.by` cannot select more than one column.
i The following 2 columns were selected:
i group1 and group2
Code
print(tl_isotonic)
Message
-- Probability Calibration
Method: Isotonic regression
Type: Binary
Source class:
Data points: 4,000, split in 8 groups
Unique Predicted Values: 86
Truth variable: `class`
Estimate variables:
`.pred_class_1` ==> class_1
`.pred_class_2` ==> class_2
Code
print(mtnl_isotonic)
Message
-- Probability Calibration
Method: Isotonic regression
Type: Multiclass (1 v All)
Source class:
Data points: 5,000, split in 10 groups
Truth variable: `class`
Estimate variables:
`.pred_one` ==> one
`.pred_two` ==> two
`.pred_three` ==> three
x This function does not work with grouped data frames.
i Apply `dplyr::ungroup()` and use the `.by` argument.
Code
print(sl_logistic)
Message
-- Probability Calibration
Method: Isotonic regression
Type: Regression
Source class: Data Frame
Data points: 2,000
Unique Predicted Values: 43
Truth variable: `outcome`
Estimate variables:
`.pred` ==> predictions
Code
print(sl_logistic_group)
Message
-- Probability Calibration
Method: Isotonic regression
Type: Regression
Source class: Data Frame
Data points: 2,000, split in 10 groups
Unique Predicted Values: 11
Truth variable: `outcome`
Estimate variables:
`.pred` ==> predictions
x `.by` cannot select more than one column.
i The following 2 columns were selected:
i group1 and group2
Code
print(sl_boot)
Message
-- Probability Calibration
Method: Bootstrapped isotonic regression
Type: Binary
Source class: Data Frame
Data points: 1,010
Truth variable: `Class`
Estimate variables:
`.pred_good` ==> good
`.pred_poor` ==> poor
Code
print(sl_boot_group)
Message
-- Probability Calibration
Method: Bootstrapped isotonic regression
Type: Binary
Source class: Data Frame
Data points: 1,010, split in 2 groups
Truth variable: `Class`
Estimate variables:
`.pred_good` ==> good
`.pred_poor` ==> poor
x `.by` cannot select more than one column.
i The following 2 columns were selected:
i group1 and group2
Code
print(tl_isotonic)
Message
-- Probability Calibration
Method: Bootstrapped isotonic regression
Type: Binary
Source class: Tune Results
Data points: 4,000, split in 8 groups
Truth variable: `class`
Estimate variables:
`.pred_class_1` ==> class_1
`.pred_class_2` ==> class_2
Code
print(mtnl_isotonic)
Message
-- Probability Calibration
Method: Bootstrapped isotonic regression
Type: Multiclass (1 v All)
Source class: Tune Results
Data points: 5,000, split in 10 groups
Truth variable: `class`
Estimate variables:
`.pred_one` ==> one
`.pred_two` ==> two
`.pred_three` ==> three
x This function does not work with grouped data frames.
i Apply `dplyr::ungroup()` and use the `.by` argument.
Code
print(sl_beta)
Message
-- Probability Calibration
Method: Beta calibration
Type: Binary
Source class: Data Frame
Data points: 1,010
Truth variable: `Class`
Estimate variables:
`.pred_good` ==> good
`.pred_poor` ==> poor
Code
print(sl_beta_group)
Message
-- Probability Calibration
Method: Beta calibration
Type: Binary
Source class: Data Frame
Data points: 1,010, split in 2 groups
Truth variable: `Class`
Estimate variables:
`.pred_good` ==> good
`.pred_poor` ==> poor
x `.by` cannot select more than one column.
i The following 2 columns were selected:
i group1 and group2
Code
print(tl_beta)
Message
-- Probability Calibration
Method: Beta calibration
Type: Binary
Source class: Tune Results
Data points: 4,000, split in 8 groups
Truth variable: `class`
Estimate variables:
`.pred_class_1` ==> class_1
`.pred_class_2` ==> class_2
Code
print(mtnl_isotonic)
Message
-- Probability Calibration
Method: Beta calibration
Type: Multiclass (1 v All)
Source class: Tune Results
Data points: 5,000, split in 10 groups
Truth variable: `class`
Estimate variables:
`.pred_one` ==> one
`.pred_two` ==> two
`.pred_three` ==> three
x This function does not work with grouped data frames.
i Apply `dplyr::ungroup()` and use the `.by` argument.
Code
print(sp_multi)
Message
-- Probability Calibration
Method: Multinomial regression
Type: Multiclass
Source class: Data Frame
Data points: 110
Truth variable: `Species`
Estimate variables:
`.pred_bobcat` ==> bobcat
`.pred_coyote` ==> coyote
`.pred_gray_fox` ==> gray_fox
Code
print(sp_smth_multi)
Message
-- Probability Calibration
Method: Generalized additive model
Type: Multiclass
Source class: Data Frame
Data points: 110
Truth variable: `Species`
Estimate variables:
`.pred_bobcat` ==> bobcat
`.pred_coyote` ==> coyote
`.pred_gray_fox` ==> gray_fox
Code
print(sl_multi_group)
Message
-- Probability Calibration
Method: Multinomial regression
Type: Multiclass
Source class: Data Frame
Data points: 110, split in 2 groups
Truth variable: `Species`
Estimate variables:
`.pred_bobcat` ==> bobcat
`.pred_coyote` ==> coyote
`.pred_gray_fox` ==> gray_fox
x `.by` cannot select more than one column.
i The following 2 columns were selected:
i group1 and group2
Code
print(tl_multi)
Message
-- Probability Calibration
Method: Multinomial regression
Type: Multiclass
Source class: Tune Results
Data points: 5,000, split in 10 groups
Truth variable: `class`
Estimate variables:
`.pred_one` ==> one
`.pred_two` ==> two
`.pred_three` ==> three
Code
print(tl_smth_multi)
Message
-- Probability Calibration
Method: Generalized additive model
Type: Multiclass
Source class: Tune Results
Data points: 5,000, split in 10 groups
Truth variable: `class`
Estimate variables:
`.pred_one` ==> one
`.pred_two` ==> two
`.pred_three` ==> three
x This function does not work with grouped data frames.
i Apply `dplyr::ungroup()` and use the `.by` argument.
Code
print(sl_logistic)
Message
-- Regression Calibration
Method: Linear
Source class: Data Frame
Data points: 2,000
Truth variable: `outcome`
Estimate variable: `.pred`
Code
print(sl_logistic_group)
Message
-- Regression Calibration
Method: Linear
Source class: Data Frame
Data points: 2,000, split in 2 groups
Truth variable: `outcome`
Estimate variable: `.pred`
x `.by` cannot select more than one column.
i The following 2 columns were selected:
i group1 and group2
Code
print(tl_linear)
Message
-- Regression Calibration
Method: Linear
Source class: Tune Results
Data points: 750, split in 10 groups
Truth variable: `outcome`
Estimate variable: `.pred`
x This function does not work with grouped data frames.
i Apply `dplyr::ungroup()` and use the `.by` argument.
Code
print(sl_gam)
Message
-- Regression Calibration
Method: Generalized additive model
Source class: Data Frame
Data points: 2,000
Truth variable: `outcome`
Estimate variable: `.pred`
Code
print(sl_gam_group)
Message
-- Regression Calibration
Method: Generalized additive model
Source class: Data Frame
Data points: 2,000, split in 2 groups
Truth variable: `outcome`
Estimate variable: `.pred`
x `.by` cannot select more than one column.
i The following 2 columns were selected:
i group1 and group2
Code
print(tl_gam)
Message
-- Regression Calibration
Method: Generalized additive model
Source class: Tune Results
Data points: 750, split in 10 groups
Truth variable: `outcome`
Estimate variable: `.pred`
Code
cal_estimate_isotonic(new_segment, Class, c(good, poor))
Message
-- Probability Calibration
Method: Isotonic regression
Type: Binary
Source class: Data Frame
Data points: 1,010
Unique Predicted Values: 66
Truth variable: `Class`
Estimate variables:
`good` ==> good
`poor` ==> poor
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