Interactive Exploration with Explorapp

Launching Explorapp

explorapp() opens the interactive Shiny application for visual exploration of classification decision boundaries. Launch it from the R console:

library(classbound)

# Empty app (start with simulation or drawing)
explorapp()

# Pre-loaded dataset
library(palmerpenguins)
penguins <- na.omit(palmerpenguins::penguins)
explorapp(data = penguins, target_col = "species")

When data and target_col are provided, Import Data mode is selected automatically.

Data modes

The sidebar’s Data Mode panel controls which dataset the models are trained on.

Import Data

Available when you call explorapp(data = ..., target_col = ...). The provided data frame is loaded directly. Use Data Preview to inspect the loaded rows.

For datasets with more than two features, a 2D Slice or Projection tab appears in the visualization area (see below).

Simulate Data

Generate synthetic data from one of two engines:

Multivariate Normal (MVN): You specify the number of classes, and the app generates class-specific mean and covariance sliders. Click Generate Data to draw a new dataset. A separate independent test set is also generated with exactly 30% of the training sample size. This is not a 70/30 split; it is an additional independently drawn evaluation sample using the same class parameters.

MixSim: Generates more complex overlapping Gaussian mixture data. Specify the number of classes K, number of dimensions p, and maximum pairwise overlap MaxOmega. Smaller overlap values create more clearly separated classes.

Both engines support a Random Seed for reproducibility and a Background Noise slider for adding uniformly distributed contamination points.

Draw Data

Draw your own classification dataset by clicking or brushing directly on the plot. Choose from two interaction modes:

Draw Point: Each click adds one observation at the clicked location. Dragging does not create a stroke; only the location where you release the mouse counts.

Draw Cluster: Dragging creates a cluster of observations along the drawn path. The Point Density and Brush Size controls determine how many points are added per path segment.

Use Undo Last to remove the most recently added batch of points. Clear Canvas removes all drawn points.

Clone to Draw Canvas copies a supported 2-feature dataset (from Import or Simulate) onto the Draw Canvas so you can continue editing it. High-dimensional datasets cannot be cloned because the Draw Canvas is a 2-feature interface.

Model configuration

The Model Configuration panel lists all available classifiers. Select one or more to compare simultaneously. Each classifier shows its own tuning parameters when selected.

Supported classifiers include: - rpart (decision tree): complexity parameter cp - randomForest: number of trees, mtry - PPtreeViz / PPtreeExtclass / PPtreeExt_split: projection pursuit trees - ppforest2: projection pursuit random forest - Tidymodels presets (if the parsnip package is installed): Decision Tree, Random Forest, SVM, Neural Net, PP Forest

Grid Resolution controls the density of the prediction grid: higher values produce smoother boundaries at the cost of computation time. 100 is a good default.

Visualization modes

Navigate

The default interaction mode. Use Ctrl + mouse wheel to zoom in/out, or draw a brush (drag) on the plot to select a region and zoom into it. Double-click to reset the view.

2D Slice

For datasets with more than two features, the 2D Slice tab lets you choose which two features to display on the axes. All other numeric features are fixed at their training-set median and categorical features at their mode.

The slice shows the decision boundary at a specific cross-section. Changing the selected features reveals different slices through the multi-dimensional boundary.

Projection

The Projection tab uses a PCA-derived basis to project the high-dimensional feature space into two axes. Unlike a slice, the projection combines information from all features simultaneously.

When a projection is active, training observations are depth-faded: points closer to the projection plane appear more opaque, while points further away fade toward transparency.

Probability surface

The Probability Surface option (in Visual Settings) shades decision regions by the model’s predicted class probability: deep colors indicate high confidence and faded colors indicate uncertainty near the boundary.

Probability surface is only available when the selected classifier provides class probabilities. Classifiers such as standard SVMs and PPtree models do not provide probabilities and always produce flat colored regions regardless of this setting. The app displays: “Probability surface unavailable: the selected model does not provide class probabilities.”

Outlier injection

The Outlier Injection panel adds extreme observations to the training data to test how classifiers respond to unusual points.

Outlier Class: the class label assigned to the injected outlier, or Random for a randomly chosen class.
Outlier Magnitude: controls how far the outlier is placed from its class distribution.
Number of Outliers: how many outliers to inject at once.
Highlight Outliers (Diamonds): renders injected outliers with a distinct diamond shape.

Injected outliers become part of the training data. When models are refitted, the outliers influence the fitted boundary. For simulated data, outliers are added to the training set and do not affect the independently generated test set.

Use Clear to remove all injected outliers without clearing the rest of the dataset.

Performance metrics

The Training Performance Metrics table at the bottom of the main panel shows accuracy, precision, recall, and F1 for each fitted model on the training data.

The Test Error column reports error on the independently generated test dataset (for Simulate Data mode). This test set is generated fresh using the same simulation parameters as the training data, with approximately 30% of the training sample size. It is not derived by splitting the training data.

For Import Data and Draw Data modes, no automatic test data is fabricated. Test Error is not reported for these modes.

Export

Click Export Results… to open the Export Wizard, which lets you choose which components to include:

Component	Description
Data (CSV)	The current training dataset (including any injected outliers)
Fitted Models (RDS)	All currently fitted model objects as an R list
Plots (PNG / PDF)	Current boundary plots as image files
Grid (CSV)	The raw boundary prediction grid (x, y, prediction, probabilities)
Metrics (CSV)	The performance metrics table
Reproduce Script (R)	An auto-generated R script that reloads the data and redraws the plots

The reproduce script loads data.csv and models.rds from the same folder and calls boundary_compute() and plot_boundary() to regenerate the plots. It captures the current grid resolution, zoom state, and visualization settings. UI-only state (e.g., panel layout, color theme preferences) is intentionally not exported.

Import Workspace Models

The Import Workspace Models panel scans your R Global Environment for workflow, model_fit, and model_spec objects (from tidymodels). Any found objects can be imported into the app for comparison against the built-in classifiers.