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https://github.com/alexpghayes/safepredict
Consistent prediction following tidymodels principles
https://github.com/alexpghayes/safepredict
Last synced: 28 days ago
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Consistent prediction following tidymodels principles
- Host: GitHub
- URL: https://github.com/alexpghayes/safepredict
- Owner: alexpghayes
- License: other
- Created: 2018-08-29T19:08:30.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2018-12-27T06:11:59.000Z (almost 6 years ago)
- Last Synced: 2024-07-31T19:28:09.699Z (3 months ago)
- Language: R
- Homepage: https://alexpghayes.github.io/safepredict/
- Size: 346 KB
- Stars: 17
- Watchers: 3
- Forks: 1
- Open Issues: 14
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
out.width = "100%"
)
options(tibble.print_min = 5, tibble.print_max = 5)
set.seed(27)
```
# safepredict
[](https://travis-ci.org/alexpghayes/safepredict)
[](https://codecov.io/github/alexpghayes/safepredict?branch=master)
[](https://www.tidyverse.org/lifecycle/#experimental)
`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][pred_spec].
[pred_spec]: https://tidymodels.github.io/model-implementation-principles/model-predictions.html
## Installation
`safepredict` is currently in the beginning stages of development and is available only on Github. You can install it with:
```r
# install.packages("devtools")
devtools::install_github("alexpghayes/safepredict")
```
## Arguments
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 |
## Examples
Suppose you fit a logistic regression using `glm`:
```{r}
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:
```{r}
library(safepredict)
test <- tibble(x = rnorm(10, 2))
safe_predict(fit, new_data = test, type = "prob")
```
or can jump straight to hard class decisions
```{r}
safe_predict(fit, new_data = test, type = "class")
```
We can also get predictions on the link scale:
```{r}
safe_predict(fit, new_data = test, type = "link")
```
or we can get confidence intervals on the response scale
```{r}
safe_predict(fit, new_data = test, type = "conf_int")
```
## Related work
- [`parsnip`](https://tidymodels.github.io/parsnip/) provides a consistent interface to supervised models. Eventually `safepredict` will act as the prediction backend for `parsnip`.
- [`broom`](https://broom.tidyverse.org/) summarizes key information about models in tidy tibbles.
- The many ongoing [`tidymodels`](https://github.com/tidymodels) projects.
- The [`prediction`](https://github.com/leeper/prediction) package by Thomas Leeper. `prediction` supports a much wider variety of models than `safepredict` at the moment, but makes fewer consistency guarantees.