plot.predicted_df.Rd
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", diag_color = "red", target)
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 yaxis will have the same spacing. 
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  Length2 character vector: colors to fill density curves.
Default is c("firebrick", "steelblue"). If named, names must match

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 
conf_colors  Length2 character vector: colors to fill density curves. Default is c("black", "steelblue"). 
text_color  Character: color to write percent correct. Default is "yellow". 
diag_color  Character: color to highlight main diagonal. These are correct predictions. Default is "red". 
A ggplot object
Note that a ggplot object is returned, so you can do additional customization of the plot. See the third example.
# Some regression examples models < machine_learn(pima_diabetes[1:50, ], patient_id, outcome = plasma_glucose, models = "rf", tune = FALSE)#>#>#> #>#> #>#>#>#> #>#>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 = 14)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)#>#>#> #>#> #>#>#>#> #>#>predict(class_models, risk_groups = c("v low", "low", "medium", "high", "very high")) %>% plot()