Visualize Model (Unified 1D/2D)
VisualizerModel.RdThis class provides a unified interface for visualizing machine learning models on both 1D and 2D tasks. It automatically detects the dimensionality and creates appropriate visualizations using ggplot2.
Super class
vistool::Visualizer -> VisualizerModel
Public fields
task(`mlr3::Task`)
Task used to train the model.learner(`mlr3::Learner`)
Learner used to train the model.
Methods
Method new()
Creates a new instance of this [R6][R6::R6Class] class.
Usage
VisualizerModel$new(
task,
learner,
x1_limits = NULL,
x2_limits = NULL,
padding = 0,
n_points = 100L
)Arguments
task([mlr3::Task])
The task to train the model on.learner(`mlr3::Learner`)
The learner to train the model with.x1_limits(`numeric(2)`)
Limits for the first feature axis. For 1D tasks, this controls the x-axis range. If NULL, will be determined from task data.x2_limits(`numeric(2)`)
Limits for the second feature axis (2D tasks only). Ignored for 1D tasks. If NULL, will be determined from task data.padding(`numeric(1)`)
A margin that is added to x1limits and x2limits. The x1 margin is calculated by `max(x1lmits) - min(x1limits) * padding`.padding(`numeric(1)`)
A margin that is added to x1limits and x2limits. The x1 margin is calculated by `max(x1lmits) - min(x1limits) * padding`.n_points(`integer(1)`)
The number of generated point per dimension. Note that a grid of `npoints^2` values is generated and evaluated by `objective$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$eval(x)` to plot the surface.
Method add_training_data()
Adds the training data to the plot.
Usage
VisualizerModel$add_training_data(
color = "auto",
size = NULL,
shape = 19,
alpha = NULL,
show_labels = FALSE,
label_size = NULL
)Arguments
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"`.size(`numeric(1)`)
Size of the points. If NULL, uses theme$point_size. Default is NULL.shape(`numeric(1)` or named `numeric`)
Shape of the points. For classification tasks, can be a named vector mapping class labels to shapes. Default is 19 (filled circle).alpha(`numeric(1)`)
Alpha transparency of the points. If NULL, uses theme$alpha. Default is NULL.show_labels(`logical(1)`)
Whether to show data point labels. Default is FALSE.label_size(`numeric(1)`)
Size of data point labels. If NULL, defaults to smaller text.
Method add_boundary()
Adds boundary lines/contours to the plot.
Usage
VisualizerModel$add_boundary(
values = NULL,
color = "black",
linetype = "dashed",
linewidth = NULL,
alpha = NULL
)Arguments
values(`numeric()`)
Values at which to draw boundaries. For 1D: horizontal lines (y-values). For 2D: contour lines (z-values). If NULL, uses sensible defaults based on prediction type.color(`character(1)`)
Color of the boundary lines. Default is "black".linetype(`character(1)`)
Line type for boundaries. For 1D: ggplot2 linetypes. For 2D: contour line types. Default is "dashed".linewidth(`numeric(1)`)
Width of boundary lines. If NULL, uses theme$line_width.alpha(`numeric(1)`)
Alpha transparency of boundary lines. If NULL, uses theme$alpha.
Method plot()
Create and return the ggplot2 plot with model-specific layers.