README.md
In alexpghayes/safepredict: Consistent prediction following tidymodels principles

safepredict

safepredict has two goals: to provide a consistent interface to prediction via the safe_predict() generic, and to accurately quantify prediction uncertainty.

safe_predict():

always returns a tibble.
never drops rows with missing data.
always uses the same arguments in the same order.

safepredict follows the tidymodels prediction specification.

safepredict is currently in the beginning stages of development and is available only on Github. You can install it with:

# install.packages("devtools")
devtools::install_github("alexpghayes/safepredict")

The three main arguments to safe_predict() are always the same:

object: A model object you would like to get predictions from.
new_data: Required. Data in the same format as required for the predict() method.
type: What kind of predictions you would like. Options are:

| type | application | | ---------- | ------------------------------------------- | | response | numeric predictions | | class | hard class predictions | | prob | class probabilities, survivor probabilities | | link | glm linear predictor | | conf_int | confidence intervals | | pred_int | prediction intervals |

Suppose you fit a logistic regression using glm:

library(tibble)

data <- tibble(
  y = as.factor(rep(c("A", "B"), each = 50)),
  x = c(rnorm(50, 1), rnorm(50, 3))
)

fit <- glm(y ~ x, data, family = binomial)

You can predict class probabilities:

library(safepredict)

test <- tibble(x = rnorm(10, 2))

safe_predict(fit, new_data = test, type = "prob")
#> # A tibble: 10 x 2
#>   .pred_A .pred_B
#>     <dbl>   <dbl>
#> 1  0.333    0.667
#> 2  0.0410   0.959
#> 3  0.619    0.381
#> 4  0.467    0.533
#> 5  0.132    0.868
#> # ... with 5 more rows

or can jump straight to hard class decisions

safe_predict(fit, new_data = test, type = "class")
#> # A tibble: 10 x 1
#>   .pred_class
#>   <fct>      
#> 1 B          
#> 2 B          
#> 3 A          
#> 4 B          
#> 5 B          
#> # ... with 5 more rows

We can also get predictions on the link scale:

safe_predict(fit, new_data = test, type = "link")
#> # A tibble: 10 x 1
#>    .pred
#>    <dbl>
#> 1  0.696
#> 2  3.15 
#> 3 -0.485
#> 4  0.132
#> 5  1.88 
#> # ... with 5 more rows

or we can get confidence intervals on the response scale

safe_predict(fit, new_data = test, type = "conf_int")
#> # A tibble: 10 x 3
#>   .pred .pred_lower .pred_upper
#>   <dbl>       <dbl>       <dbl>
#> 1 0.667       0.795       0.510
#> 2 0.959       0.989       0.862
#> 3 0.381       0.545       0.240
#> 4 0.533       0.680       0.380
#> 5 0.868       0.943       0.724
#> # ... with 5 more rows

parsnip provides a consistent interface to supervised models. Eventually safepredict will act as the prediction backend for parsnip.
broom summarizes key information about models in tidy tibbles.
The many ongoing tidymodels projects.
The prediction package by Thomas Leeper. prediction supports a much wider variety of models than safepredict at the moment, but makes fewer consistency guarantees.