Package: densemlp 0.6.0
densemlp: Dense Neural Networks for Tabular Classification and Regression
Provides dense feed-forward neural network models for tabular regression and classification using 'torch'. The package supports modern extensions around dense neural network blocks, including dropout, batch normalization, residual connections, gated blocks, and optional input projection.
Authors:
densemlp_0.6.0.tar.gz
densemlp_0.6.0.zip(r-4.7-any)densemlp_0.6.0.zip(r-4.6-any)densemlp_0.6.0.zip(r-4.5-any)
densemlp_0.6.0.tgz(r-4.6-any)densemlp_0.6.0.tgz(r-4.5-any)
densemlp_0.6.0.tar.gz(r-4.7-any)densemlp_0.6.0.tar.gz(r-4.6-any)
densemlp_0.6.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
densemlp/json (API)
| # Install 'densemlp' in R: |
| install.packages('densemlp', repos = c('https://ielbadisy.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ielbadisy/densemlp/issues
classificationdeep-learningmlpregressiontabular-datatorch
Last updated from:8c41a4289a. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 155 | ||
| source / vignettes | OK | 201 | ||
| linux-release | OK | 160 | ||
| macos-release | OK | 103 | ||
| macos-oldrel | OK | 107 | ||
| windows-devel | OK | 109 | ||
| windows-release | OK | 119 | ||
| windows-oldrel | OK | 94 | ||
| wasm-release | OK | 112 |
Exports:cv_densemlpdensemlpdensemlp_metricsperm_importanceplot_historytune_densemlp
Dependencies:bitbit64callrclicorocpp11descfarverggplot2gluegtableisobandjsonlitelabelinglifecyclemagrittrotelprocessxpsR6RColorBrewerRcpprlangS7safetensorsscalestorchvctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Plot training history | autoplot.densemlp_fit |
| Cross-validate a dense multilayer perceptron | cv_densemlp |
| Fit a tabular dense multilayer perceptron | densemlp |
| Compute densemlp metrics | densemlp_metrics |
| Permutation variable importance | perm_importance |
| Plot training history | plot_history |
| Plot permutation importance | plot.densemlp_importance |
| Predict from a fitted dense multilayer perceptron | predict.densemlp_fit |
| Print a fitted dense multilayer perceptron | print.densemlp_fit |
| Tune a dense multilayer perceptron over a task-aware hyperparameter grid | tune_densemlp |
