Showing 8 of total 8 results (show query)
ohdsi
PatientLevelPrediction:Develop Clinical Prediction Models Using the Common Data Model
A user friendly way to create patient level prediction models using the Observational Medical Outcomes Partnership Common Data Model. Given a cohort of interest and an outcome of interest, the package can use data in the Common Data Model to build a large set of features. These features can then be used to fit a predictive model with a number of machine learning algorithms. This is further described in Reps (2017) <doi:10.1093/jamia/ocy032>.
Maintained by Egill Fridgeirsson. Last updated 24 days ago.
6.9 match 190 stars 10.85 score 297 scripts
bioc
BiocSklearn:interface to python sklearn via Rstudio reticulate
This package provides interfaces to selected sklearn elements, and demonstrates fault tolerant use of python modules requiring extensive iteration.
Maintained by Vince Carey. Last updated 5 months ago.
statisticalmethoddimensionreductioninfrastructure
15.5 match 4.34 score 11 scripts
akai01
ngboostForecast:Probabilistic Time Series Forecasting
Probabilistic time series forecasting via Natural Gradient Boosting for Probabilistic Prediction.
Maintained by Resul Akay. Last updated 3 years ago.
forecastingmachine-learningngboostngboost-forecastprobabilistic-forecastspythonsklearntime-series
11.0 match 7 stars 3.69 score 14 scripts
dyfanjones
sagemaker:R SDK for `AWS Sagemaker`
A library for training and deploying machine learning models on Amazon SageMaker <https://aws.amazon.com/sagemaker/> using R through `paws SDK`.
Maintained by Dyfan Jones. Last updated 3 years ago.
amazon-sagemakerawsmachine-learningsagemakersdk
3.3 match 12 stars 2.78 score 6 scripts
techtonique
nnetsauce:Randomized and Quasi-Randomized networks for Statistical/Machine Learning
Randomized and Quasi-Randomized networks for Statistical/Machine Learning
Maintained by T. Moudiki. Last updated 7 months ago.
deep-learningmachine-learningneural-networksrandomized-algorithmsstatistical-learning
3.3 match 2 stars 2.60 score 6 scripts
dzhakparov
GeneSelectR:Comprehensive Feature Selection Worfkflow for Bulk RNAseq Datasets
GeneSelectR is a versatile R package designed for efficient RNA sequencing data analysis. Its key innovation lies in the seamless integration of the Python sklearn machine learning framework with R-based bioinformatics tools. This integration enables GeneSelectR to perform robust ML-driven feature selection while simultaneously leveraging the power of Gene Ontology (GO) enrichment and semantic similarity analyses. By combining these diverse methodologies, GeneSelectR offers a comprehensive workflow that optimizes both the computational aspects of ML and the biological insights afforded by advanced bioinformatics analyses. Ideal for researchers in bioinformatics, GeneSelectR stands out as a unique tool for analyzing complex RNAseq datasets with enhanced precision and relevance.
Maintained by Damir Zhakparov. Last updated 10 months ago.
1.7 match 19 stars 4.98 score 7 scriptsdyfanjones
sagemaker.mlframework:sagemaker machine learning developed by amazon
`sagemaker` machine learning developed by amazon.
Maintained by Dyfan Jones. Last updated 3 years ago.
amazon-sagemakerawsmachine-learningsagemakersdk
3.3 match 2.48 score 2 dependents
rnorm
CatEncoders:Encoders for Categorical Variables
Contains some commonly used categorical variable encoders, such as 'LabelEncoder' and 'OneHotEncoder'. Inspired by the encoders implemented in Python 'sklearn.preprocessing' package (see <http://scikit-learn.org/stable/modules/preprocessing.html>).
Maintained by nl zhang. Last updated 8 years ago.
0.5 match 1 stars 2.48 score 25 scripts 4 dependents