Showing 10 of total 10 results (show query)
tidymodels
recipes:Preprocessing and Feature Engineering Steps for Modeling
A recipe prepares your data for modeling. We provide an extensible framework for pipeable sequences of feature engineering steps provides preprocessing tools to be applied to data. Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting processed output can then be used as inputs for statistical or machine learning models.
Maintained by Max Kuhn. Last updated 3 days ago.
586 stars 18.80 score 7.2k scripts 383 dependentstidymodels
tune:Tidy Tuning Tools
The ability to tune models is important. 'tune' contains functions and classes to be used in conjunction with other 'tidymodels' packages for finding reasonable values of hyper-parameters in models, pre-processing methods, and post-processing steps.
Maintained by Max Kuhn. Last updated 27 days ago.
293 stars 14.27 score 756 scripts 39 dependents
r-lib
generics:Common S3 Generics not Provided by Base R Methods Related to Model Fitting
In order to reduce potential package dependencies and conflicts, generics provides a number of commonly used S3 generics.
Maintained by Hadley Wickham. Last updated 1 years ago.
61 stars 14.00 score 131 scripts 9.8k dependentstidymodels
textrecipes:Extra 'Recipes' for Text Processing
Converting text to numerical features requires specifically created procedures, which are implemented as steps according to the 'recipes' package. These steps allows for tokenization, filtering, counting (tf and tfidf) and feature hashing.
Maintained by Emil Hvitfeldt. Last updated 12 days ago.
160 stars 10.86 score 964 scripts 1 dependentstidymodels
themis:Extra Recipes Steps for Dealing with Unbalanced Data
A dataset with an uneven number of cases in each class is said to be unbalanced. Many models produce a subpar performance on unbalanced datasets. A dataset can be balanced by increasing the number of minority cases using SMOTE 2011 <doi:10.48550/arXiv.1106.1813>, BorderlineSMOTE 2005 <doi:10.1007/11538059_91> and ADASYN 2008 <https://ieeexplore.ieee.org/document/4633969>. Or by decreasing the number of majority cases using NearMiss 2003 <https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf> or Tomek link removal 1976 <https://ieeexplore.ieee.org/document/4309452>.
Maintained by Emil Hvitfeldt. Last updated 2 months ago.
143 stars 10.37 score 1.3k scripts 2 dependentstidymodels
rules:Model Wrappers for Rule-Based Models
Bindings for additional models for use with the 'parsnip' package. Models include prediction rule ensembles (Friedman and Popescu, 2008) <doi:10.1214/07-AOAS148>, C5.0 rules (Quinlan, 1992 ISBN: 1558602380), and Cubist (Kuhn and Johnson, 2013) <doi:10.1007/978-1-4614-6849-3>.
Maintained by Emil Hvitfeldt. Last updated 6 months ago.
40 stars 9.52 score 20k scripts 1 dependentstidymodels
embed:Extra Recipes for Encoding Predictors
Predictors can be converted to one or more numeric representations using a variety of methods. Effect encodings using simple generalized linear models <doi:10.48550/arXiv.1611.09477> or nonlinear models <doi:10.48550/arXiv.1604.06737> can be used. There are also functions for dimension reduction and other approaches.
Maintained by Emil Hvitfeldt. Last updated 2 months ago.
142 stars 9.18 score 1.1k scriptstidymodels
brulee:High-Level Modeling Functions with 'torch'
Provides high-level modeling functions to define and train models using the 'torch' R package. Models include linear, logistic, and multinomial regression as well as multilayer perceptrons.
Maintained by Max Kuhn. Last updated 11 days ago.
69 stars 7.56 score 214 scripts
egenn
rtemis:Machine Learning and Visualization
Advanced Machine Learning and Visualization. Unsupervised Learning (Clustering, Decomposition), Supervised Learning (Classification, Regression), Cross-Decomposition, Bagging, Boosting, Meta-models. Static and interactive graphics.
Maintained by E.D. Gennatas. Last updated 2 months ago.
data-sciencedata-visualizationmachine-learningmachine-learning-libraryvisualization
145 stars 7.09 score 50 scripts 2 dependents
jameshwade
measure:A Recipes-style Interface to Tidymodels for Analytical Measurements
Analytical measurements...
Maintained by James Wade. Last updated 1 months ago.
5 stars 5.06 score 58 scripts