The main goal of
tidypredict is to enable running predictions inside
databases. It reads the model, extracts the components needed to
calculate the prediction, and then creates an R formula that can be
translated into SQL. In other words, it is able to parse a model such as
model <- lm(mpg ~ wt + cyl, data = mtcars)
tidypredict can return a SQL statement that is ready to run inside the
database. Because it uses
dplyr’s database interface, it works with
several databases back-ends, such as MS SQL:
## <SQL> 39.6862614802529 + (`wt` * -3.19097213898374) + (`cyl` * -1.5077949682598)
tidypredict from CRAN using:
Or install the development version using
devtools as follows:
# install.packages("remotes") # remotes::install_github("tidymodels/tidypredict")
tidypredict has only a few functions, and it is not expected that
number to grow much. The main focus at this time is to add more models
||Returns an R formula that calculates the prediction|
||Returns a SQL query based on the formula from
||Adds a new column using the formula from
||Creates a list spec based on the R model|
||Prepares an object to be recognized as a parsed model|
Instead of translating directly to a SQL statement,
creates an R formula. That formula can then be used inside
overall workflow would be as illustrated in the image above, and
tidypredictreads model, and creates a list object with the necessary components to run predictions
tidypredictbuilds an R formula based on the list object
dplyrevaluates the formula created by
dplyrtranslates the formula into a SQL statement, or any other interfaces.
tidypredict writes and reads a spec based on a model. Instead of
simply writing the R formula directly, splitting the spec from the
formula adds the following capabilities:
.rds- Specifically for cases when the model needs to be used for predictions in a Shiny app.
tidypredict. It also means, that the parsed model spec can become a good alternative to using PMML.
The following models are supported by
tidypredict supports models fitted via the
parsnip interface. The
ones confirmed currently work in
linear_reg()with “lm” as the engine.
rand_forest()with “randomForest” as the engine.
rand_forest()with “ranger” as the engine.
mars()with “earth” as the engine.
tidy() function from broom works with linear models parsed via
pm <- parse_model(lm(wt ~ ., mtcars)) tidy(pm)
## # A tibble: 11 x 2 ## term estimate ## <chr> <dbl> ## 1 (Intercept) -0.231 ## 2 mpg -0.0417 ## 3 cyl -0.0573 ## 4 disp 0.00669 ## 5 hp -0.00323 ## 6 drat -0.0901 ## 7 qsec 0.200 ## 8 vs -0.0664 ## 9 am 0.0184 ## 10 gear -0.0935 ## 11 carb 0.249
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