Showing 4 of total 4 results (show query)
dselivanov
text2vec:Modern Text Mining Framework for R
Fast and memory-friendly tools for text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), similarities. This package provides a source-agnostic streaming API, which allows researchers to perform analysis of collections of documents which are larger than available RAM. All core functions are parallelized to benefit from multicore machines.
Maintained by Dmitriy Selivanov. Last updated 8 months ago.
glovelatent-dirichlet-allocationnatural-language-processingtext-miningtopic-modelingvectorizationword-embeddingsword2veccpp
860 stars 13.48 score 1.3k scripts 23 dependents
winvector
vtreat:A Statistically Sound 'data.frame' Processor/Conditioner
A 'data.frame' processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. 'vtreat' prepares variables so that data has fewer exceptional cases, making it easier to safely use models in production. Common problems 'vtreat' defends against: 'Inf', 'NA', too many categorical levels, rare categorical levels, and new categorical levels (levels seen during application, but not during training). Reference: "'vtreat': a data.frame Processor for Predictive Modeling", Zumel, Mount, 2016, <DOI:10.5281/zenodo.1173313>.
Maintained by John Mount. Last updated 3 months ago.
categorical-variablesmachine-learning-algorithmsnested-modelsprepare-data
285 stars 11.19 score 328 scripts 1 dependentsdselivanov
mlapi:Abstract Classes for Building 'scikit-learn' Like API
Provides 'R6' abstract classes for building machine learning models with 'scikit-learn' like API. <https://scikit-learn.org/> is a popular module for 'Python' programming language which design became de facto a standard in industry for machine learning tasks.
Maintained by Dmitriy Selivanov. Last updated 3 years ago.
5.36 score 5 scripts 24 dependents
jackdunnnz
iai:Interface to 'Interpretable AI' Modules
An interface to the algorithms of 'Interpretable AI' <https://www.interpretable.ai> from the R programming language. 'Interpretable AI' provides various modules, including 'Optimal Trees' for classification, regression, prescription and survival analysis, 'Optimal Imputation' for missing data imputation and outlier detection, and 'Optimal Feature Selection' for exact sparse regression. The 'iai' package is an open-source project. The 'Interpretable AI' software modules are proprietary products, but free academic and evaluation licenses are available.
Maintained by Jack Dunn. Last updated 6 months ago.
1 stars 2.00 score 7 scripts