fit_best_learner.md
In postcard: Estimating Marginal Effects with Prognostic Covariate Adjustment

`get_best_learner` print information when verbose > 0 [plain]

Code
  get_best_learner(resamples = cv_folds, learners = learners, preproc = list(mod = y ~
    x1), verbose = 2)
Message
  i Fitting learners
  * mod_mars
  * mod_lm
  i No tuning parameters. `fit_resamples()` will be attempted
  i 1 of 2 resampling: mod_mars
  v 1 of 2 resampling: mod_mars ()
  i No tuning parameters. `fit_resamples()` will be attempted
  i 2 of 2 resampling: mod_lm
  v 2 of 2 resampling: mod_lm ()
  i Model with lowest RMSE: mod_lm
Output
  == Workflow ====================================================================
  Preprocessor: Formula
  Model: linear_reg()

  -- Preprocessor ----------------------------------------------------------------
  y ~ x1

  -- Model -----------------------------------------------------------------------
  Linear Regression Model Specification (regression)

  Computational engine: lm

`get_best_learner` print information when verbose > 0 [ansi]

Code
  get_best_learner(resamples = cv_folds, learners = learners, preproc = list(mod = y ~
    x1), verbose = 2)
Message
  [36mi[39m Fitting learners
  * mod_mars
  * mod_lm
  [34mi[39m   [30mNo tuning parameters. `fit_resamples()` will be attempted[39m
  [34mi[39m [30m1 of 2 resampling: mod_mars[39m
  [32mv[39m [30m1 of 2 resampling: mod_mars[39m[30m ()[39m
  [34mi[39m   [30mNo tuning parameters. `fit_resamples()` will be attempted[39m
  [34mi[39m [30m2 of 2 resampling: mod_lm[39m
  [32mv[39m [30m2 of 2 resampling: mod_lm[39m[30m ()[39m
  [36mi[39m Model with lowest RMSE: mod_lm
Output
  == Workflow ====================================================================
  [3mPreprocessor:[23m Formula
  [3mModel:[23m linear_reg()

  -- Preprocessor ----------------------------------------------------------------
  y ~ x1

  -- Model -----------------------------------------------------------------------
  Linear Regression Model Specification (regression)

  Computational engine: lm

`get_best_learner` print information when verbose > 0 [unicode]

Code
  get_best_learner(resamples = cv_folds, learners = learners, preproc = list(mod = y ~
    x1), verbose = 2)
Message
  ℹ Fitting learners
  • mod_mars
  • mod_lm
  i No tuning parameters. `fit_resamples()` will be attempted
  i 1 of 2 resampling: mod_mars
  ✔ 1 of 2 resampling: mod_mars ()
  i No tuning parameters. `fit_resamples()` will be attempted
  i 2 of 2 resampling: mod_lm
  ✔ 2 of 2 resampling: mod_lm ()
  ℹ Model with lowest RMSE: mod_lm
Output
  ══ Workflow ════════════════════════════════════════════════════════════════════
  Preprocessor: Formula
  Model: linear_reg()

  ── Preprocessor ────────────────────────────────────────────────────────────────
  y ~ x1

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

  Computational engine: lm

`get_best_learner` print information when verbose > 0 [fancy]

Code
  get_best_learner(resamples = cv_folds, learners = learners, preproc = list(mod = y ~
    x1), verbose = 2)
Message
  [36mℹ[39m Fitting learners
  • mod_mars
  • mod_lm
  [34mi[39m   [30mNo tuning parameters. `fit_resamples()` will be attempted[39m
  [34mi[39m [30m1 of 2 resampling: mod_mars[39m
  [32m✔[39m [30m1 of 2 resampling: mod_mars[39m[30m ()[39m
  [34mi[39m   [30mNo tuning parameters. `fit_resamples()` will be attempted[39m
  [34mi[39m [30m2 of 2 resampling: mod_lm[39m
  [32m✔[39m [30m2 of 2 resampling: mod_lm[39m[30m ()[39m
  [36mℹ[39m Model with lowest RMSE: mod_lm
Output
  ══ Workflow ════════════════════════════════════════════════════════════════════
  [3mPreprocessor:[23m Formula
  [3mModel:[23m linear_reg()

  ── Preprocessor ────────────────────────────────────────────────────────────────
  y ~ x1

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

  Computational engine: lm