[This article was first published on T. Moudiki's Webpage - R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
This post is about LSBoost, an Explainable ‘AI’ algorithm which uses Gradient Boosted randomized networks for pattern recognition. As we’ve discussed it last week LSBoost is a cousin of GFAGBM’s LS_Boost. In LSBoost, more specifically, the so called weak learners from LS_Boost are based on randomized neural networks’ components and variants of Least Squares regression models.
I’ve already presented some promising examples of use of LSBoost based on Ridge Regression weak learners. In mlsauce’s version 0.7.1, the Lasso can also be used as an alternative ingredient to the weak learners. Here is a comparison of the regression coefficients obtained by using mlsauce’s implementation of Ridge regression and the Lasso:
R example: LSBoostRegressor with Ridge regression and the Lasso
The following example is about training set error vs testing set error, as a function of the regularization parameter, both for Ridge regression and Lasso-based weak learners.
Packages and data
# 0 - Packages and data -------------------------------------------------------
library(devtools)
devtools::install_github("thierrymoudiki/mlsauce/R-package")
library(mlsauce)
library(datasets)
print(summary(datasets::mtcars))
X <- as.matrix(datasets::mtcars[, -1])
y <- as.integer(datasets::mtcars[, 1])
n <- dim(X)[1]
p <- dim(X)[2]
set.seed(21341)
train_index <- sample(x = 1:n, size = floor(0.8*n), replace = TRUE)
test_index <- -train_index
X_train <- as.matrix(X[train_index, ])
y_train <- as.double(y[train_index])
X_test <- as.matrix(X[test_index, ])
y_test <- as.double(y[test_index])
LSBoost using Ridge regression
# 1 - Ridge -------------------------------------------------------------------
obj <- mlsauce::LSBoostRegressor() # default h is Ridge
print(obj$get_params())
n_lambdas <- 100
lambdas <- 10**seq(from=-6, to=6,
length.out = n_lambdas)
rmse_matrix <- matrix(NA, nrow = 2, ncol = n_lambdas)
rownames(rmse_matrix) <- c("training rmse", "testing rmse")
for (j in 1:n_lambdas)
{
obj$set_params(reg_lambda = lambdas[j])
obj$fit(X_train, y_train)
rmse_matrix[, j] <- c(sqrt(mean((obj$predict(X_train) - y_train)**2)),
sqrt(mean((obj$predict(X_test) - y_test)**2)))
}
LSBoost using the Lasso
# 2 - Lasso -------------------------------------------------------------------
obj <- mlsauce::LSBoostRegressor(solver = "lasso")
print(obj$get_params())
n_lambdas <- 100
lambdas <- 10**seq(from=-6, to=6,
length.out = n_lambdas)
rmse_matrix2 <- matrix(NA, nrow = 2, ncol = n_lambdas)
rownames(rmse_matrix2) <- c("training rmse", "testing rmse")
for (j in 1:n_lambdas)
{
obj$set_params(reg_lambda = lambdas[j])
obj$fit(X_train, y_train)
rmse_matrix2[, j] <- c(sqrt(mean((obj$predict(X_train) - y_train)**2)),
sqrt(mean((obj$predict(X_test) - y_test)**2)))
}
R session info
> print(session_info())
─ Session info ─────────────────────────────────────────────────────────────
setting value
version R version 4.0.2 (2020-06-22)
os Ubuntu 16.04.6 LTS
system x86_64, linux-gnu
ui RStudio
language (EN)
collate C.UTF-8
ctype C.UTF-8
tz Etc/UTC
date 2020-07-31
─ Packages ─────────────────────────────────────────────────────────────────
package * version date lib source
assertthat 0.2.1 2019-03-21 [1] RSPM (R 4.0.2)
backports 1.1.8 2020-06-17 [1] RSPM (R 4.0.2)
callr 3.4.3 2020-03-28 [1] RSPM (R 4.0.2)
cli 2.0.2 2020-02-28 [1] RSPM (R 4.0.2)
crayon 1.3.4 2017-09-16 [1] RSPM (R 4.0.2)
curl 4.3 2019-12-02 [1] RSPM (R 4.0.2)
desc 1.2.0 2018-05-01 [1] RSPM (R 4.0.2)
devtools * 2.3.1 2020-07-21 [1] RSPM (R 4.0.2)
digest 0.6.25 2020-02-23 [1] RSPM (R 4.0.2)
ellipsis 0.3.1 2020-05-15 [1] RSPM (R 4.0.2)
fansi 0.4.1 2020-01-08 [1] RSPM (R 4.0.2)
fs 1.4.2 2020-06-30 [1] RSPM (R 4.0.2)
glue 1.4.1 2020-05-13 [1] RSPM (R 4.0.2)
jsonlite 1.7.0 2020-06-25 [1] RSPM (R 4.0.2)
lattice 0.20-41 2020-04-02 [2] CRAN (R 4.0.2)
magrittr 1.5 2014-11-22 [1] RSPM (R 4.0.2)
Matrix 1.2-18 2019-11-27 [2] CRAN (R 4.0.2)
memoise 1.1.0 2017-04-21 [1] RSPM (R 4.0.2)
mlsauce * 0.7.1 2020-07-31 [1] Github (thierrymoudiki/mlsauce@68e391a)
pkgbuild 1.1.0 2020-07-13 [1] RSPM (R 4.0.2)
pkgload 1.1.0 2020-05-29 [1] RSPM (R 4.0.2)
prettyunits 1.1.1 2020-01-24 [1] RSPM (R 4.0.2)
processx 3.4.3 2020-07-05 [1] RSPM (R 4.0.2)
ps 1.3.3 2020-05-08 [1] RSPM (R 4.0.2)
R6 2.4.1 2019-11-12 [1] RSPM (R 4.0.2)
rappdirs 0.3.1 2016-03-28 [1] RSPM (R 4.0.2)
Rcpp 1.0.5 2020-07-06 [1] RSPM (R 4.0.2)
remotes 2.2.0 2020-07-21 [1] RSPM (R 4.0.2)
reticulate 1.16 2020-05-27 [1] RSPM (R 4.0.2)
rlang 0.4.7 2020-07-09 [1] RSPM (R 4.0.2)
rprojroot 1.3-2 2018-01-03 [1] RSPM (R 4.0.2)
rstudioapi 0.11 2020-02-07 [1] RSPM (R 4.0.2)
sessioninfo 1.1.1 2018-11-05 [1] RSPM (R 4.0.2)
testthat 2.3.2 2020-03-02 [1] RSPM (R 4.0.2)
usethis * 1.6.1 2020-04-29 [1] RSPM (R 4.0.2)
withr 2.2.0 2020-04-20 [1] RSPM (R 4.0.2)
[1] /home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0
[2] /opt/R/4.0.2/lib/R/library
No post in August
To leave a comment for the author, please follow the link and comment on their blog: T. Moudiki's Webpage - R.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
from R-bloggers https://ift.tt/2Ezm6at
via IFTTT
Comments
Post a Comment