Visualize Model as Interactive Surface

This class is used to create interactive 3D surface visualizations of learners and tasks for 2D input data using plotly.

Super classes

vistool::Visualizer -> vistool::VisualizerSurface -> VisualizerSurfaceModel

Public fields

task

(`mlr3::Task`)
Task used to train the model.

learner

(`mlr3::Learner`)
Learner used to train the model.

Methods

Public methods

• VisualizerSurfaceModel$new()

• VisualizerSurfaceModel$add_training_data()

• VisualizerSurfaceModel$add_boundary()

• VisualizerSurfaceModel$plot()

• VisualizerSurfaceModel$clone()

Inherited methods

• vistool::Visualizer$add_points()
• vistool::Visualizer$resolve_layer_colors()
• vistool::Visualizer$save()
• vistool::Visualizer$set_theme()
• vistool::Visualizer$theme()
• vistool::VisualizerSurface$add_contours()
• vistool::VisualizerSurface$init_layer_contour()
• vistool::VisualizerSurface$init_layer_surface()
• vistool::VisualizerSurface$set_layout()
• vistool::VisualizerSurface$set_scene()

---

Method new()

Creates a new instance of this [R6][R6::R6Class] class.

Usage

VisualizerSurfaceModel$new(
  task,
  learner,
  x1_limits = NULL,
  x2_limits = NULL,
  padding = 0,
  n_points = 100L
)

Arguments

task

([mlr3::Task])
The task to train the model on.

learner

(`mlr3::Learner`)
The learner to train the model with.

x1_limits

(`numeric(2)`)
The x1 limits.

x1_limits

(`numeric(2)`)
The x1 limits.

x2_limits

(`numeric(2)`)
The x2 limits.

x2_limits

(`numeric(2)`)
The x2 limits.

padding

(`numeric(1)`)
A margin that is added to x1limits and x2limits. The x1 margin is calculated by `max(x1lmits) - min(x1limits) * padding`.

padding

(`numeric(1)`)
A margin that is added to x1limits and x2limits. The x1 margin is calculated by `max(x1lmits) - min(x1limits) * padding`.

n_points

(`integer(1)`)
The number of generated point per dimension. Note that a grid of `npoints^2` values is generated and evaluated by `objective$eval(x)` to plot the surface.

n_points

(`integer(1)`)
The number of generated point per dimension. Note that a grid of `npoints^2` values is generated and evaluated by `objective$eval(x)` to plot the surface.

---

Method add_training_data()

Adds the training data to the plot.

Usage

VisualizerSurfaceModel$add_training_data(
  size = NULL,
  color = "auto",
  shape = 0,
  ...
)

Arguments

size

(`numeric(1)`)
Size of the points. If NULL, uses theme$point_size. Default is NULL.

color

(`character(1)` or named `character`)
Color of the points. For classification tasks: - `character(1)`: A single color for all points (e.g., `"blue"`) or `"auto"` for automatic color assignment. - `named character`: A vector mapping class labels to colors (e.g., `c(pos = "red", neg = "blue")`). For regression tasks, only single colors are supported. Default is `"auto"`.

shape

(`numeric(1)` or named `numeric`)
Shape/symbol of the points. For classification tasks: - `numeric(1)`: A single symbol for all points (e.g., `0` for circle). - `named numeric`: A vector mapping class labels to symbols (e.g., `c(pos = 0, neg = 1)`). For regression tasks, only single symbols are supported. Default is 0 (circle).

...

(`any`)
Further arguments passed to `add_trace(...)`.

---

Method add_boundary()

Adds boundary surface(s) to the plot at specified values.

Usage

VisualizerSurfaceModel$add_boundary(values = NULL, color = NULL, ...)

Arguments

values

(`numeric()`)
Vector of z-values where to draw boundary surfaces. For classification with probability predictions, defaults to 0.5. For regression or response predictions, defaults to the median of predictions.

color

(`character(1)` or `list()`)
Color specification for boundary surfaces. Default uses a neutral colorscale.

...

(`any`)
Further arguments passed to `add_trace(...)` or `add_surface(...)`.

---

Method plot()

Create and return the plotly plot with model-specific layers.

Usage

VisualizerSurfaceModel$plot(...)

Arguments

...

Additional arguments passed to the parent plot method.

Returns

A plotly object.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

VisualizerSurfaceModel$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.