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RemixAutoML

AdrianAntico / RemixAutoML

Star 202

R package for automation of machine learning, forecasting, feature engineering, model evaluation, model interpretation, recommenders, and EDA.

r timeseries h2o regression supervised-learning xgboost classification gpu-acceleration lightgbm recommender-system feature-engineering unsupervised-learning automl panel-data multiclass-classification automated-machine-learning anomaly-detection catboost intermittent-demand funnel-data
  • Updated Aug 7, 2022
  • R
OmicSelector

kstawiski / OmicSelector

Star 19

OmicSelector - Environment, docker-based application and R package for biomarker signiture selection (feature selection) & deep learning diagnostic tool development from high-throughput high-throughput omics experiments and other multidimensional datasets. Initially developed for miRNA-seq, RNA-seq and qPCR.

data-science data-mining bioinformatics rna-seq r deep-neural-networks deep-learning neural-network docker-image next-generation-sequencing variable-selection feature-selection bioinformatics-pipeline classification r-package omics biomarker-discovery qpcr mirna biomarkers
  • Updated Jun 11, 2022
  • R
ELeFHAnt

praneet1988 / ELeFHAnt

Star 17

Ensemble Learning for Harmonization and Annotation of Single Cells (ELeFHAnt) provides an easy to use R package for users to annotate clusters of single cells, harmonize labels across single cell datasets to generate a unified atlas and infer relationship among celltypes between two datasets. It uses an ensemble of three machine learning classifiers 1) RF 2) SVM and 3) LR

machine-learning scalability classification single-cell annotation-tool harmonization ngs-analysis single-cell-rna-seq cell-biology celltype-annotation
  • Updated Jul 18, 2022
  • R

longhaiSK / HTLR

Star 8
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Explanation of initial state parameter?

4
GabeAl
GabeAl commented Nov 26, 2019

Hello,

I'd like to use this package with extremely high-dimensional datasets, which aren't supported by glmnet because of its 4gb integer/array size limitation. Therefore I want to know how I can specify the initial state ("init" parameter of htlr() ) for the markov chain, and what format this variable can take.

For example, I would like to use the biglasso package on millions of features a

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