Package 'shinymodels'

Description

This object has the results when a neural network was tuned using Bayesian optimization and a validation set.

Details

The code used to produce this object:

  data(ames)

  ames <-
    ames %>%
    select(Sale_Price, Neighborhood, Longitude, Latitude, Year_Built) %>%
    mutate(Sale_Price = log10(ames$Sale_Price))

  set.seed(1)
  ames_rs <- validation_split(ames)

  ames_rec <-
    recipe(Sale_Price ~ ., data = ames) %>%
    step_dummy(all_nominal_predictors()) %>%
    step_zv(all_predictors()) %>%
    step_normalize(all_predictors())

  mlp_spec <-
    mlp(hidden_units = tune(),
        penalty = tune(),
        epochs = tune()) %>%
    set_mode("regression")

  set.seed(1)
  ames_mlp_itr <-
    mlp_spec %>%
    tune_bayes(
      ames_rec,
      resamples = ames_rs,
      initial = 5,
      iter = 4,
      control = control_bayes(save_pred = TRUE)
    )

Value

An object with primary class iteration_results.

Description

This object has the results when a bagged regression tree was resampled using 10-fold cross-validation.

Details

The code used to produce this object:

  library(tidymodels)
  library(baguette)
  tidymodels_prefer()

  # ------------------------------------------------------------------------------

  ctrl_rs <- control_resamples(save_pred = TRUE)

  # ------------------------------------------------------------------------------

  set.seed(1)
  cars_rs <- vfold_cv(mtcars)

  cars_bag_vfld <-
    bag_tree() %>%
    set_engine("rpart", times = 5) %>%
    set_mode("regression") %>%
    fit_resamples(
      mpg ~ .,
      resamples = cars_rs,
      control = ctrl_rs
    )

Value

An object with primary class resample_results.

Description

This object has the results when a CART classification tree model was tuned over the cost-complexity parameter using racing.

Details

To reduce the object size, a smaller subset of the data were used.

The code used to produce this object:

  library(tidymodels)
  library(finetune)
  tidymodels_prefer()

  ctrl_rc <- control_race(save_pred = TRUE)

  # ------------------------------------------------------------------------------

  data(cells)

  set.seed(1)
  cells <-
    cells %>%
    select(-case) %>%
    sample_n(200)

  # ------------------------------------------------------------------------------

  set.seed(2)
  cell_rs <- vfold_cv(cells)

  # ------------------------------------------------------------------------------

  set.seed(3)
  cell_race <-
    decision_tree(cost_complexity = tune()) %>%
    set_mode("classification") %>%
    tune_race_anova(
      class ~ .,
      resamples = cell_rs,
      grid = tibble(cost_complexity = 10^seq(-2, -1, by = 0.2)),
      control = ctrl_rc
    )

Value

An object with primary class tune_race.

Description

explore() launches a Shiny application to interact with results from some tidymodels functions.

To investigate model fit(s), explore() can be used on objects produced by

• tune::fit_resamples()

• tune::tune_grid()

• tune::tune_bayes()

• finetune::tune_sim_anneal()

• finetune::tune_race_anova()

• finetune::tune_race_win_loss()

• tune::last_fit()

The application starts in a new window and allows users to see how predicted values align with the true, observed data. There are 2-3 tabs in the application (depending on the object):

• Tuning Parameters enables users to choose a specific set of tuning parameters. These results are shown in the Plots tab. The default configuration is based on the optimal value of the first performance metric used during the creation of the object.

• Plots shows various panels that can visualize how well the model fits. Specific points can be highlighted by clicking on them (as long as the hover_only = FALSE option was used). To reset the highlighted points, double on the graph background.

• About gives information on the application as well as links to get help or file bug reports/feature requests.

To quit the Shiny application, use the Esc key.

Usage

## Default S3 method:
explore(x, ...)

## S3 method for class 'tune_results'
explore(x, hover_cols = NULL, hover_only = FALSE, ...)

Arguments

Details

For resampling methods that produce more than one hold-out prediction per row (e.g. the bootstrap, repeated V-fold cross-validation), the predicted values shown in the plots are averages of the predictions for that specific row.

The ggplot2 theme used in the Shiny application corresponds to the current theme in the R session. Run ggplot2::theme_set() to change the theme for the plots in the Shiny application.

For classification models, there is a toggle on the bottom left of the application to choose between "Unscaled (i.e. linear)" and "Logit scaled" probability scaling. The first options plots the raw probabilities while the logit scaling uses scales::logit_trans() to rescale the axis. This can be helpful when a model with a linear predictor is used (e.g. logistic or multinomial regression) since it can show linear effects from a feature more easily.

When using the application, there may be warnings printed in the console about "event tied a source ID ... not registered". These can be ignored.

When racing results are explored, the shiny application will only allow tuning parameter combinations that were fully resampled. As a result, parameter combinations that were discarded during the race will not be able to be selected.

Value

A shiny application.

Examples

data(ames_mlp_itr)

if (interactive()) {
  explore(ames_mlp_itr, hover_cols = dplyr::contains("tude"))
}

Description

This object has the results when a flexible discriminant analysis model was tuned over the interaction degree parameters.

Details

To reduce the object size, five bootstraps were used for resampling and missing data were removed.

The code used to produce this object:

  library(tidymodels)
  library(discrim)
  tidymodels_prefer()

  # ------------------------------------------------------------------------------

  ctrl_gr <- control_grid(save_pred = TRUE)

  # ------------------------------------------------------------------------------

  data(scat)
  scat <- scat[complete.cases(scat), ]

  # ------------------------------------------------------------------------------

  set.seed(1)
  scat_rs <- bootstraps(scat, times = 5)

  scat_fda_bt <-
    discrim_flexible(prod_degree = tune()) %>%
    tune_grid(
      Species ~ .,
      resamples = scat_rs,
      control = ctrl_gr
    )

Value

An object with primary class tune_results.

Description

This object has the results when a logistic regression model is fit to the training set and is evaluated on the test set.

Details

The code used to produce this object:

  library(tidymodels)
  tidymodels_prefer()

  # ------------------------------------------------------------------------------

  set.seed(1)
  data(two_class_dat)

  # ------------------------------------------------------------------------------

  two_class_split <- initial_split(two_class_dat)
  # ------------------------------------------------------------------------------

  glm_spec <- logistic_reg()

  two_class_final <-
    glm_spec %>%
    last_fit(
      Class  ~ .,
      split = two_class_split
    )

Value

An object with primary class last_fit.