Introduction | Installation of Keras 3 in R | 1. Install the R Package | 2. Environment Setup by Operating System | Linux (Ubuntu / Debian / Fedora) | Windows | Example Workflow: Non-Linear Synthetic Regression | Step 1: Preprocessing Machine and Data Splitting with mlfeaturer | Step 2: Building and Compiling the Keras Model | Step 3: Model Training | Step 4: Model Evaluation on Scaled Space | Step 5: Back-Transformation and Evaluation on Physical Scale | Acknowledgments | References
Package: mlfeaturer 0.4.3
mlfeaturer: Machine Learning Feature Engineering with R
Utilities for scaling and transformation of features in the context of machine learning regression problems.
Authors:
mlfeaturer_0.4.3.tar.gz
mlfeaturer_0.4.3.zip(r-4.7-any)mlfeaturer_0.4.3.zip(r-4.6-any)mlfeaturer_0.4.3.zip(r-4.5-any)
mlfeaturer_0.4.3.tgz(r-4.6-any)mlfeaturer_0.4.3.tgz(r-4.5-any)
mlfeaturer_0.4.3.tar.gz(r-4.7-any)mlfeaturer_0.4.3.tar.gz(r-4.6-any)
mlfeaturer_0.4.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
mlfeaturer/json (API)
| # Install 'mlfeaturer' in R: |
| install.packages('mlfeaturer', repos = c('https://tpetzoldt.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/tpetzoldt/mlfeaturer/issues
Pkgdown/docs site:https://tpetzoldt.github.io
Datasets:
- dauta4 - Growthrate of Algae
Last updated from:0d715879bd. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 126 | ||
| source / vignettes | OK | 241 | ||
| linux-release | OK | 130 | ||
| macos-release | OK | 164 | ||
| macos-oldrel | OK | 147 | ||
| windows-devel | OK | 90 | ||
| windows-release | OK | 150 | ||
| windows-oldrel | OK | 115 | ||
| wasm-release | OK | 111 |
Exports:asas.listcreate_preprocessed_dataget_dataget_id_allget_id_testget_id_trainget_x_allget_x_testget_x_trainget_y_allget_y_testget_y_traininv_transform_dataml_evaluatepredictresidualsrsquaredscale_dataset_transformationstransform_data
Dependencies:clicpp11dplyrevaluategenericsgluehighrjsonliteknitrlifecyclelubridatemagrittrpillarpkgconfigpurrrR6rlangstringistringrtibbletidyrtidyselecttimechangeutf8vctrswithrxfunyaml
03 - Neural Networks with keras
Last update: 2026-08-07
Started: 2026-08-07
01 - Transformation of Features with Package mlfeaturer
Introduction | Core Objectives | Target Audience & Use Cases | Installation | Example Workflow: Preprocessing Microalgae Growth Data | The dauta4 Dataset | Step 1: Scaling and Transformation Pipeline | Step 2: Extracting Preprocessing States | Step 3: Visualizing Relationships Across Preprocessing States | Step 4: Model Fitting and Prediction Back-Transformation | Step 5: Evaluating Model Predictions on Physical Scale | Summary & Next Steps | Acknowledgments | References
Last update: 2026-08-07
Started: 2026-08-07
02 - Neural Networks with mlfeaturer and nnet
Introduction | Installation | Prepare Data | Fit a Neural Network | Evaluate the Network | Graphical Comparison of Results | Acknowledgments | References
Last update: 2026-08-07
Started: 2026-08-07
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Machine Learning and Feature Engineering for Regression Models | mlfeaturer-package mlfeaturer |
| Convert feature_data to feature_data_list | as |
| Convert feature_data to a list | as.list,feature_data-method |
| Create Preprocessed Data Object | create_preprocessed_data |
| Growthrate of Algae | dauta4 |
| List Representation of Preprocessed Data | feature_data_list-class |
| Preprocessed Data | feature_data-class |
| Preprocessing Parameters | feature_params-class |
| Get All Data (X and Y) | get_data get_data,feature_data-method |
| Get ID Columns for all data | get_id_all get_id_all,feature_data-method |
| Get ID Columns for test data | get_id_test get_id_test,feature_data-method |
| Get ID Columns for training data | get_id_train get_id_train,feature_data-method |
| Get All Data (X) | get_x_all get_x_all,feature_data-method |
| Get Test Data (X) | get_x_test get_x_test,feature_data-method |
| Get Training Data (X) | get_x_train get_x_train,feature_data-method |
| Get All Data (Y) | get_y_all get_y_all,feature_data-method |
| Get Test Data (Y) | get_y_test get_y_test,feature_data-method |
| Get Training Data (Y) | get_y_train get_y_train,feature_data-method |
| Inverse Transform Data | inv_transform_data inv_transform_data,data.frame,feature_params,missing-method inv_transform_data,feature_data,ANY,ANY-method inv_transform_data,feature_data,missing,list-method |
| Union Class of List or NULL | list_or_NULL-class |
| Evaluate Machine Learning Model Performance | ml_evaluate ml_evaluate,feature_data-method |
| Model Predictions with `feature_data` Objects | predict,feature_data-method |
| Extract Model Residuals Using `feature_data` Objects | residuals,feature_data-method |
| Nonlinear Coefficient of Determination Using `feature_data` Objects | rsquared rsquared,feature_data-method |
| Scale Data | scale_data scale_data,data.frame,feature_params-method scale_data,feature_data,missing-method |
| Set Transformations | set_transformations set_transformations,feature_data-method |
| Transform Data | transform_data transform_data,data.frame,feature_params,missing-method transform_data,data.frame,missing,list-method transform_data,feature_data,ANY,ANY-method transform_data,feature_data,missing,list-method |