Extract a model fitted workflow from a tidyAML model tibble.
Arguments
- .data
The model table that must have the class
tidyaml_mod_spec_tbl
.- .model_id
The model number that you want to select, Must be an integer or sequence of integers, ie.
1
orc(1,3,5)
or1:2
Details
This function allows you to get a model fitted workflow or more from
a tibble with a class of "tidyaml_mod_spec_tbl". It allows you to select the
model by the .model_id
column. You can call the model id's by an integer
or a sequence of integers.
See also
Other Extractor:
extract_model_spec()
,
extract_regression_residuals()
,
extract_wflw_pred()
,
extract_wflw()
,
get_model()
Examples
library(recipes)
rec_obj <- recipe(mpg ~ ., data = mtcars)
frt_tbl <- fast_regression(mtcars, rec_obj, .parsnip_eng = c("lm","glm"),
.parsnip_fns = "linear_reg")
extract_wflw_fit(frt_tbl, 1)
#> [[1]]
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#>
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 0 Recipe Steps
#>
#> ── Model ───────────────────────────────────────────────────────────────────────
#>
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#>
#> Coefficients:
#> (Intercept) cyl disp hp drat wt
#> 16.367158 -0.071944 0.019601 -0.028662 -0.008186 -5.512757
#> qsec vs am gear carb
#> 0.893780 0.057990 0.822540 1.090844 0.295341
#>
#>
extract_wflw_fit(frt_tbl, 1:2)
#> [[1]]
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#>
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 0 Recipe Steps
#>
#> ── Model ───────────────────────────────────────────────────────────────────────
#>
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#>
#> Coefficients:
#> (Intercept) cyl disp hp drat wt
#> 16.367158 -0.071944 0.019601 -0.028662 -0.008186 -5.512757
#> qsec vs am gear carb
#> 0.893780 0.057990 0.822540 1.090844 0.295341
#>
#>
#> [[2]]
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#>
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 0 Recipe Steps
#>
#> ── Model ───────────────────────────────────────────────────────────────────────
#>
#> Call: stats::glm(formula = ..y ~ ., family = stats::gaussian, data = data)
#>
#> Coefficients:
#> (Intercept) cyl disp hp drat wt
#> 16.367158 -0.071944 0.019601 -0.028662 -0.008186 -5.512757
#> qsec vs am gear carb
#> 0.893780 0.057990 0.822540 1.090844 0.295341
#>
#> Degrees of Freedom: 23 Total (i.e. Null); 13 Residual
#> Null Deviance: 677.3
#> Residual Deviance: 75.8 AIC: 119.7
#>