Plot model predictions vs observed outcomes

# S3 method for predicted_df
plot(
  x,
  caption = TRUE,
  title = NULL,
  font_size = 11,
  outcomes = NULL,
  fixed_aspect = attr(x, "model_info")$type == "Regression",
  print = TRUE,
  ...
)

plot_regression_predictions(x, point_size = 1, point_alpha = 1, target)

plot_classification_predictions(
  x,
  fill_colors = c("firebrick", "steelblue"),
  fill_alpha = 0.7,
  curve_flex = 1,
  add_labels = TRUE,
  target
)

plot_multiclass_predictions(
  x,
  conf_colors = c("black", "steelblue"),
  text_color = "yellow",
  text_size = 3,
  text_angle = 60,
  diag_color = "red",
  target
)

Arguments

x	data frame as returned `predict.model_list`
caption	Put model performance in plot caption? TRUE (default) prints all available metrics, FALSE prints nothing. Can also provide metric name (e.g. "RMSE"), in which case the caption will include only that metric.
title	Character: Plot title, default NULL produces no title.
font_size	Number: Relative size of all font in plot, default = 11
outcomes	Vector of outcomes if not present in x
fixed_aspect	Logical: If TRUE (default for regression only), units of the x- and y-axis will have the same spacing.
print	Logical, if TRUE (default) the plot is printed on the current graphics device. The plot is always (silently) returned.
...	Parameters specific to plot_regression_predictions or plot_classification_predictions; listed below. These must be named.
point_size	Number: Point size, relative to 1
point_alpha	Number in [0, 1] giving point opacity
target	Not meant to be set by user. outcome column name
fill_colors	Length-2 character vector: colors to fill density curves. Default is c("firebrick", "steelblue"). If named, names must match `unique(x[[target]])`, in any order.
fill_alpha	Number in [0, 1] giving opacity of fill colors.
curve_flex	Numeric. Kernal adjustment for density curves. Default is 1. Less than 1 makes curves more flexible, analogous to smaller bins in a histogram; greater than 1 makes curves more rigid.
add_labels	If TRUE (default) and a predicted_group column was added to predictions by specifying `risk_groups` or `outcome_groups` in `link{predict.model_list}`, labels specifying groups are added to the plot.
conf_colors	Length-2 character vector: colors to fill density curves. Default is c("black", "steelblue").
text_color	Character: color to write percent correct. Default is "yellow".
text_size	Numeric or logical: size of percent correct text. Defaults to 3, a readable size. Greater than 20 classes might need smaller text. Text can be turned off by setting to FALSE.
text_angle	Numeric or logical: angle to rotate x axis text. Defaults to 60 degrees. Setting to FALSE will turn text horizontal.
diag_color	Character: color to highlight main diagonal. These are correct predictions. Default is "red".

Value

A ggplot object

Details

Note that a ggplot object is returned, so you can do additional customization of the plot. See the third example.

Examples

# Some regression examples
models <- machine_learn(pima_diabetes[1:50, ], patient_id, outcome = plasma_glucose,
                        models = "rf", tune = FALSE)
#> Training new data prep recipe...
#> Variable(s) ignored in prep_data won't be used to tune models: patient_id
#> 
#> plasma_glucose looks numeric, so training regression algorithms.
#> 
#> After data processing, models are being trained on 14 features with 50 observations.
#> Based on n_folds = 5 and hyperparameter settings, the following number of models will be trained: 5 rf's 
#> Training at fixed values: Random Forest
#> 
#> *** Models successfully trained. The model object contains the training data minus ignored ID columns. ***
#> *** If there was PHI in training data, normal PHI protocols apply to the model object. ***
predictions <- predict(models)
plot(predictions)
plot(predictions, caption = "Rsquared",
     title = "This model's predictions regress to the mean",
     point_size = 3, point_alpha = .7, font_size = 9)
p <- plot(predictions, print = FALSE)
p + theme_classic()

# A classification example with risk groups
class_models <- machine_learn(pima_diabetes, patient_id, outcome = diabetes,
                              models = "xgb", tune = FALSE)
#> Training new data prep recipe...
#> Variable(s) ignored in prep_data won't be used to tune models: patient_id
#> 
#> diabetes looks categorical, so training classification algorithms.
#> 
#> After data processing, models are being trained on 12 features with 768 observations.
#> Based on n_folds = 5 and hyperparameter settings, the following number of models will be trained: 5 xgb's 
#> Training at fixed values: eXtreme Gradient Boosting
#> 
#> *** Models successfully trained. The model object contains the training data minus ignored ID columns. ***
#> *** If there was PHI in training data, normal PHI protocols apply to the model object. ***
predict(class_models,
        risk_groups = c("v low", "low", "medium", "high", "very high")) %>%
  plot()