Climbing the echelons in a methodical and logical manner. Showing patience you build your career stone by stone. Finetune meaning related to workMethodical and efficient never afraid of hard work. Both logical and observant, you are not less intuitive and responsive.
Great organizational skills coupled with being rigorous and voluntary.Īt a first glance you seem cold and aloof, but you are in fact a great emotional bundle hiding its sensitivity behind a shell. Home is your recharging station that gives energy and rises your mood considerably. Daily routines are a must have for number fours because they are reassuring.Īttached to siblings and giving great importance to loved ones, your roots or country. Thoughtful, introverted, you're pretty cerebral and need to be reassured, to be comforted.
FINETUNES MEANING CODE
This project is released with a Contributor Code ofīy contributing to this project, you agree to abide by its terms.The name Finetune meaning and personality analysis. config #> #> 1 9 0.831 0.0207 roc_auc binary 0.901 1 NA Prepro… #> 2 9 0.321 0.0561 roc_auc binary 0.898 1 NA Prepro… #> # … with abbreviated variable names ¹frac_identity, ².estimator Contributing Show_best( grid_win_loss, metric = "roc_auc ", n = 2) #> ℹ Bootstrap08: 1 eliminated 9 candidates remain. #> ℹ Bootstrap05: 1 eliminated 10 candidates remain. #> ℹ Bootstrap07: 1 eliminated 11 candidates remain. #> ℹ Bootstrap01: 1 eliminated 12 candidates remain. #> ℹ Bootstrap03: 2 eliminated 13 candidates remain. #> ℹ Bootstrap04: 2 eliminated 15 candidates remain. #> ℹ Bootstrap10: 3 eliminated 17 candidates remain. #> ℹ Resamples are analyzed in a random order. #> ℹ Racing will maximize the roc_auc metric. , resamples = rs, grid = grid, control = ctrl) It treats the tuning parametersĪs sports teams in a tournament and computed win/loss statistics. Show_best( grid_anova, metric = "roc_auc ", n = 2) #> ℹ Bootstrap03: All but one parameter combination were eliminated. #> ℹ Bootstrap04: 2 eliminated 4 candidates remain. #> ℹ Bootstrap10: 14 eliminated 6 candidates remain.
Two methods here are based on those should in Kuhnįor example, using an ANOVA-type analysis to filter out parameter Testing to see which ones should be dropped or investigated more. Small set of resamples for all of the grid points, then statistically The second set of methods are for “racing”. #> # A tibble: 2 × 9 #> frac_common_cov frac_ident…¹. Show_best( sa_res, metric = "roc_auc ", n = 2) , resamples = rs, iter = 20, initial = 4, control = ctrl) # Syntax very similar to `tune_grid()` or `tune_Bayes()`: # -ĭata( two_class_dat, package = "modeldata ")
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