discrim offers various functions to fit classification models via the discriminant analysis.
As an example, we’ll use a flexible discriminant analysis model of Hastie, Tibshirani, and Buja (1994). This fits a model that uses features generated by the multivariate adaptive regression spline (MARS) model of Friedman (1991). It is able to create class boundaries that are polygons and has built-in feature selection.
The parabolic data from the modeldata package will be used to
illustrate:
library(tidymodels)
library(discrim)
tidymodels_prefer()
theme_set(theme_bw())data(parabolic, package = "modeldata")
To create the model, the discrim_flexible()
function is used along with an engine of "earth" (which contains the
methods to use the MARS model). We’ll set the number of MARS terms to
use but this can be tuned via the methods in the tune package.
The fit() function estimates the model. fit_xy() can be used if one
does not wish to use the formula method.
fda_mod <-
discrim_flexible(num_terms = 3) %>%
# increase `num_terms` to find smoother boundaries
set_engine("earth") %>%
fit(class ~ ., data = parabolic)
fda_mod
## parsnip model object
##
## Call:
## mda::fda(formula = class ~ ., data = data, method = earth::earth,
## nprune = ~3)
##
## Dimension: 1
##
## Percent Between-Group Variance Explained:
## v1
## 100
##
## Training Misclassification Error: 0.136 ( N = 500 )
Now let’s plot the class boundary by predicting on a grid of points then creating a contour plot for the 50% probability cutoff.
parabolic_grid <-
expand.grid(X1 = seq(-5, 5, length = 100),
X2 = seq(-5, 5, length = 100))parabolic_grid <-
parabolic_grid %>%
bind_cols(
predict(fda_mod, parabolic_grid, type = "prob")
)
ggplot(parabolic, aes(x = X1, y = X2)) +
geom_point(aes(col = class), alpha = .5) +
geom_contour(data = parabolic_grid, aes(z = .pred_Class1), col = "black", breaks = .5) +
coord_equal()
Maintainer: Emil Hvitfeldt emil.hvitfeldt@posit.co (ORCID)
Authors:
Max Kuhn max@posit.co (ORCID)
Other contributors:
Posit Software, PBC [copyright holder, funder]
The model function works with the tidymodels infrastructure so that the model can be resampled, tuned, tided, etc.
Useful links: