Unexpectedly different behavior for factors/dummy variables between parsnip and workflows

[juliasilge]

When training what seems like the same model (same model specification, same formula, same data) using parsnip vs. using workflows, it is surprising to see different results. I found this behavior quite unexpected, especially what workflows did.

Some options to reduce user surprise 😮 would be more clarity in the functions either in parsnip, in workflows, or both.

lm(Sepal.Length ~ ., iris)
#> 
#> Call:
#> lm(formula = Sepal.Length ~ ., data = iris)
#> 
#> Coefficients:
#>       (Intercept)        Sepal.Width       Petal.Length        Petal.Width  
#>            2.1713             0.4959             0.8292            -0.3152  
#> Speciesversicolor   Speciesvirginica  
#>           -0.7236            -1.0235

library(parsnip)
lm_spec <- linear_reg() %>%
  set_engine(engine = "lm") 

## parsnip version looks the same as lm
lm_spec %>%
  fit(Sepal.Length ~ ., data = iris)
#> parsnip model object
#> 
#> Fit time:  2ms 
#> 
#> Call:
#> stats::lm(formula = formula, data = data)
#> 
#> Coefficients:
#>       (Intercept)        Sepal.Width       Petal.Length        Petal.Width  
#>            2.1713             0.4959             0.8292            -0.3152  
#> Speciesversicolor   Speciesvirginica  
#>           -0.7236            -1.0235

## workflows version has made a different choice about dummy variables
library(workflows)
workflow() %>%
  add_model(lm_spec) %>%
  add_formula(Sepal.Length ~ .) %>%
  fit(data = iris)
#> ══ Workflow [trained] ═══════════════════════════════════════════════════════════════════════════════════════════════════════════════
#> Preprocessor: Formula
#> Model: linear_reg()
#> 
#> ── Preprocessor ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Sepal.Length ~ .
#> 
#> ── Model ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 
#> Call:
#> stats::lm(formula = formula, data = data)
#> 
#> Coefficients:
#>       (Intercept)        Sepal.Width       Petal.Length        Petal.Width  
#>            1.1478             0.4959             0.8292            -0.3152  
#>     Speciessetosa  Speciesversicolor   Speciesvirginica  
#>            1.0235             0.2999                 NA

^{Created on 2020-02-06 by the reprex package (v0.3.0)}

[juliasilge]

Yep, we are aware of where the difference arises. Current thoughts on resolving the unexpected differences are in #290.

[juliasilge]

We addressed the issue with indicator/dummy variables in #319 and tidymodels/workflows#51. Next, we need to address the difference in one-hot encoding vs. indicator/dummy variables (i.e. the intercept handling).

[juliasilge]

This has been closed in #332 and tidymodels/workflows#53.

library(parsnip)

lm_spec <- linear_reg() %>%
  set_engine(engine = "lm") 

lm_spec %>%
  fit(Sepal.Length ~ ., data = iris)
#> parsnip model object
#> 
#> Fit time:  5ms 
#> 
#> Call:
#> stats::lm(formula = Sepal.Length ~ ., data = data)
#> 
#> Coefficients:
#>       (Intercept)        Sepal.Width       Petal.Length        Petal.Width  
#>            2.1713             0.4959             0.8292            -0.3152  
#> Speciesversicolor   Speciesvirginica  
#>           -0.7236            -1.0235

library(workflows)

workflow() %>%
  add_model(lm_spec) %>%
  add_formula(Sepal.Length ~ .) %>%
  fit(data = iris)
#> ══ Workflow [trained] ═══════════════════════════════════════
#> Preprocessor: Formula
#> Model: linear_reg()
#> 
#> ── Preprocessor ─────────────────────────────────────────────
#> Sepal.Length ~ .
#> 
#> ── Model ────────────────────────────────────────────────────
#> 
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#> 
#> Coefficients:
#>       (Intercept)        Sepal.Width       Petal.Length        Petal.Width  
#>            2.1713             0.4959             0.8292            -0.3152  
#> Speciesversicolor   Speciesvirginica  
#>           -0.7236            -1.0235

^{Created on 2020-07-02 by the reprex package (v0.3.0.9001)}

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