# A tibble: 1 × 12
  r.squared adj.r.squared sigma statistic   p.value    df logLik   AIC   BIC deviance
      <dbl>         <dbl> <dbl>     <dbl>     <dbl> <dbl>  <dbl> <dbl> <dbl>    <dbl>
1     0.930         0.911  80.4      48.5   1.24e-6     3  -84.8  180.  183.   71118.
# ℹ 2 more variables: df.residual <int>, nobs <int>

# A tibble: 1 × 12
  r.squared adj.r.squared sigma statistic   p.value    df logLik   AIC   BIC deviance
      <dbl>         <dbl> <dbl>     <dbl>     <dbl> <dbl>  <dbl> <dbl> <dbl>    <dbl>
1     0.930         0.911  80.4      48.5   1.24e-6     3  -84.8  180.  183.   71118.
# ℹ 2 more variables: df.residual <int>, nobs <int>

parsnip model object

stan_glm
 family:       gaussian [identity]
 formula:      weight ~ volume * cover
 observations: 15
 predictors:   4
------
                      Median MAD_SD
(Intercept)           95.6   61.8  
volume                 0.8    0.1  
coverpaperback        -0.2    3.7  
volume:coverpaperback -0.2    0.1  

Auxiliary parameter(s):
      Median MAD_SD
sigma 85.5   17.8  

------
* For help interpreting the printed output see ?print.stanreg
* For info on the priors used see ?prior_summary.stanreg

Linear Regression Model Specification (regression)

Engine-Specific Arguments:
  prior_intercept = rstanarm::student_t(df = 1)
  prior = rstanarm::student_t(df = 1)
  seed = 1234

Computational engine: stan 

Model fit template:
rstanarm::stan_glm(formula = missing_arg(), data = missing_arg(), 
    weights = missing_arg(), prior_intercept = rstanarm::student_t(df = 1), 
    prior = rstanarm::student_t(df = 1), seed = 1234, family = stats::gaussian, 
    refresh = 0)

<Training/Testing/Total>
<150/38/188>

# A tibble: 150 × 9
   season episode title               imdb_rating total_votes air_date   month  wday top10_votes
   <fct>    <dbl> <chr>                     <dbl>       <dbl> <date>     <dbl> <dbl>       <int>
 1 8           18 Last Day in Florida         7.8        1429 2012-03-08     3     5           0
 2 9           14 Vandalism                   7.6        1402 2013-01-31     1     5           0
 3 2            8 Performance Review          8.2        2416 2005-11-15    11     3           0
 4 9            5 Here Comes Treble           7.1        1515 2012-10-25    10     5           0
 5 3           22 Beach Games                 9.1        2783 2007-05-10     5     5           0
 6 7            1 Nepotism                    8.4        1897 2010-09-23     9     5           0
 7 3           15 Phyllis' Wedding            8.3        2283 2007-02-08     2     5           0
 8 9           21 Livin' the Dream            8.9        2041 2013-05-02     5     5           0
 9 9           18 Promos                      8          1445 2013-04-04     4     5           0
10 8           12 Pool Party                  8          1612 2012-01-19     1     5           0
# ℹ 140 more rows

# A tibble: 6 × 4
  variable    type      role      source  
  <chr>       <list>    <chr>     <chr>   
1 season      <chr [2]> predictor original
2 episode     <chr [2]> predictor original
3 title       <chr [3]> ID        original
4 total_votes <chr [2]> predictor original
5 air_date    <chr [1]> predictor original
6 imdb_rating <chr [2]> outcome   original

# A tibble: 6 × 4
  variable    type      role      source  
  <chr>       <list>    <chr>     <chr>   
1 season      <chr [2]> predictor original
2 episode     <chr [2]> predictor original
3 title       <chr [3]> ID        original
4 total_votes <chr [2]> predictor original
5 air_date    <chr [1]> predictor original
6 imdb_rating <chr [2]> outcome   original

# A tibble: 6 × 4
  variable    type      role      source  
  <chr>       <list>    <chr>     <chr>   
1 season      <chr [2]> predictor original
2 episode     <chr [2]> predictor original
3 title       <chr [3]> ID        original
4 total_votes <chr [2]> predictor original
5 air_date    <chr [1]> predictor original
6 imdb_rating <chr [2]> outcome   original

# A tibble: 6 × 4
  variable    type      role      source  
  <chr>       <list>    <chr>     <chr>   
1 season      <chr [2]> predictor original
2 episode     <chr [2]> predictor original
3 title       <chr [3]> ID        original
4 total_votes <chr [2]> predictor original
5 air_date    <chr [1]> predictor original
6 imdb_rating <chr [2]> outcome   original

# A tibble: 6 × 4
  variable    type      role      source  
  <chr>       <list>    <chr>     <chr>   
1 season      <chr [2]> predictor original
2 episode     <chr [2]> predictor original
3 title       <chr [3]> ID        original
4 total_votes <chr [2]> predictor original
5 air_date    <chr [1]> predictor original
6 imdb_rating <chr [2]> outcome   original

# A tibble: 150 × 33
   episode title    imdb_rating air_date_USThanksgiv…¹ air_date_USChristmas…² air_date_USNewYearsDay
     <dbl> <fct>          <dbl>                  <int>                  <int>                  <int>
 1      18 Last Da…         7.8                      0                      0                      0
 2      14 Vandali…         7.6                      0                      0                      0
 3       8 Perform…         8.2                      0                      0                      0
 4       5 Here Co…         7.1                      0                      0                      0
 5      22 Beach G…         9.1                      0                      0                      0
 6       1 Nepotism         8.4                      0                      0                      0
 7      15 Phyllis…         8.3                      0                      0                      0
 8      21 Livin' …         8.9                      0                      0                      0
 9      18 Promos           8                        0                      0                      0
10      12 Pool Pa…         8                        0                      0                      0
# ℹ 140 more rows
# ℹ abbreviated names: ¹​air_date_USThanksgivingDay, ²​air_date_USChristmasDay
# ℹ 27 more variables: air_date_USIndependenceDay <int>, season_X2 <dbl>, season_X3 <dbl>,
#   season_X4 <dbl>, season_X5 <dbl>, season_X6 <dbl>, season_X7 <dbl>, season_X8 <dbl>,
#   season_X9 <dbl>, air_date_dow_Mon <dbl>, air_date_dow_Tue <dbl>, air_date_dow_Wed <dbl>,
#   air_date_dow_Thu <dbl>, air_date_dow_Fri <dbl>, air_date_dow_Sat <dbl>,
#   air_date_month_Feb <dbl>, air_date_month_Mar <dbl>, air_date_month_Apr <dbl>, …

                   episode                      title                imdb_rating 
                        26                        150                         26 
air_date_USThanksgivingDay    air_date_USChristmasDay     air_date_USNewYearsDay 
                         1                          1                          1 
air_date_USIndependenceDay                  season_X2                  season_X3 
                         1                          2                          2 
                 season_X4                  season_X5                  season_X6 
                         2                          2                          2 
                 season_X7                  season_X8                  season_X9 
                         2                          2                          2 
          air_date_dow_Mon           air_date_dow_Tue           air_date_dow_Wed 
                         1                          2                          1 
          air_date_dow_Thu           air_date_dow_Fri           air_date_dow_Sat 
                         2                          1                          1 
        air_date_month_Feb         air_date_month_Mar         air_date_month_Apr 
                         2                          2                          2 
        air_date_month_May         air_date_month_Jun         air_date_month_Jul 
                         2                          1                          1 
        air_date_month_Aug         air_date_month_Sep         air_date_month_Oct 
                         1                          2                          2 
        air_date_month_Nov         air_date_month_Dec                      top10 
                         2                          2                          2 

# A tibble: 150 × 22
   episode title   imdb_rating season_X2 season_X3 season_X4 season_X5 season_X6 season_X7 season_X8
     <dbl> <fct>         <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
 1      18 Last D…         7.8         0         0         0         0         0         0         1
 2      14 Vandal…         7.6         0         0         0         0         0         0         0
 3       8 Perfor…         8.2         1         0         0         0         0         0         0
 4       5 Here C…         7.1         0         0         0         0         0         0         0
 5      22 Beach …         9.1         0         1         0         0         0         0         0
 6       1 Nepoti…         8.4         0         0         0         0         0         1         0
 7      15 Phylli…         8.3         0         1         0         0         0         0         0
 8      21 Livin'…         8.9         0         0         0         0         0         0         0
 9      18 Promos          8           0         0         0         0         0         0         0
10      12 Pool P…         8           0         0         0         0         0         0         1
# ℹ 140 more rows
# ℹ 12 more variables: season_X9 <dbl>, air_date_dow_Tue <dbl>, air_date_dow_Thu <dbl>,
#   air_date_month_Feb <dbl>, air_date_month_Mar <dbl>, air_date_month_Apr <dbl>,
#   air_date_month_May <dbl>, air_date_month_Sep <dbl>, air_date_month_Oct <dbl>,
#   air_date_month_Nov <dbl>, air_date_month_Dec <dbl>, top10 <int>

══ Workflow ════════════════════════════════════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: linear_reg()

── Preprocessor ────────────────────────────────────────────────────────────────────────────────────
8 Recipe Steps

• step_date()
• step_holiday()
• step_num2factor()
• step_dummy()
• step_percentile()
• step_mutate()
• step_rm()
• step_zv()

── Model ───────────────────────────────────────────────────────────────────────────────────────────
Linear Regression Model Specification (regression)

Computational engine: lm 

══ Workflow [trained] ══════════════════════════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: linear_reg()

── Preprocessor ────────────────────────────────────────────────────────────────────────────────────
8 Recipe Steps

• step_date()
• step_holiday()
• step_num2factor()
• step_dummy()
• step_percentile()
• step_mutate()
• step_rm()
• step_zv()

── Model ───────────────────────────────────────────────────────────────────────────────────────────

Call:
stats::lm(formula = ..y ~ ., data = data)

Coefficients:
       (Intercept)             episode           season_X2           season_X3           season_X4  
          7.342550            0.007041            1.216204            1.411946            1.433737  
         season_X5           season_X6           season_X7           season_X8           season_X9  
          1.353140            1.150998            1.218278            0.526168            0.789066  
  air_date_dow_Tue    air_date_dow_Thu  air_date_month_Feb  air_date_month_Mar  air_date_month_Apr  
         -0.286676           -0.362900           -0.077280           -0.048725            0.015377  
air_date_month_May  air_date_month_Sep  air_date_month_Oct  air_date_month_Nov  air_date_month_Dec  
          0.144080            0.092809           -0.047704           -0.077198            0.306999  
             top10  
          0.890058  

# A tibble: 2 × 6
  .metric .estimator  mean     n std_err .config             
  <chr>   <chr>      <dbl> <int>   <dbl> <chr>               
1 rmse    standard   0.420     5  0.0182 Preprocessor1_Model1
2 rsq     standard   0.429     5  0.0597 Preprocessor1_Model1