autoplot.tabnet_explain: Plot tabnet_explain mask importance heatmap

In tabnet: Fit 'TabNet' Models for Classification and Regression

Plot tabnet_explain mask importance heatmap

Description

Plot tabnet_explain mask importance heatmap

Usage

## S3 method for class 'tabnet_explain'
autoplot(object, type = c("mask_agg", "steps"), quantile = 1, ...)

Arguments

object	A tabnet_explain object as a result of tabnet_explain().
type	a character value. Either "mask_agg" the default, for a single heatmap of aggregated mask importance per predictor along the dataset, or "steps" for one heatmap at each mask step.
quantile	numerical value between 0 and 1. Provides quantile clipping of the mask values
...	not used.

Details

Plot the tabnet_explain object mask importance per variable along the predicted dataset. type="mask_agg" output a single heatmap of mask aggregated values, type="steps" provides a plot faceted along the n_steps mask present in the model. quantile=.995 may be used for strong outlier clipping, in order to better highlight low values. quantile=1, the default, do not clip any values.

Value

A ggplot object.

Examples

library(ggplot2)
data("attrition", package = "modeldata")

## Single-outcome binary classification of `Attrition` in `attrition` dataset
attrition_fit <- tabnet_fit(Attrition ~. , data=attrition, epoch=11)
attrition_explain <- tabnet_explain(attrition_fit, attrition)
# Plot the model aggregated mask interpretation heatmap
autoplot(attrition_explain)

## Multi-outcome regression on `Sale_Price` and `Pool_Area` in `ames` dataset,
data("ames", package = "modeldata")
ids <- sample(nrow(ames), 256)
x <- ames[ids,-which(names(ames) %in% c("Sale_Price", "Pool_Area"))]
y <- ames[ids, c("Sale_Price", "Pool_Area")]
ames_fit <- tabnet_fit(x, y, epochs = 5, verbose=TRUE)
ames_explain <- tabnet_explain(ames_fit, x)
autoplot(ames_explain, quantile = 0.99)

tabnet documentation built on June 22, 2024, 10:22 a.m.