mlr - mlr: Machine Learning in R

•    R

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Please cite our JMLR paper [bibtex]. Some parts of the package were created as part of other publications. If you use these parts, please cite the relevant work appropriately. An overview of all mlr related publications can be found here.

machine-learning data-science tuning cran r-package predictive-modeling classification regression statistics r survival-analysis imbalance-correction tutorial mlr learners hyperparameters-optimization feature-selection multilabel-classification clustering stacking

MLJ.jl - A Julia machine learning framework

•    Jupyter

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New to MLJ? Start here. Wanting to integrate an existing machine learning model into the MLJ framework? Start here.

data-science machine-learning statistics pipeline clustering julia pipelines regression tuning classification ensemble-learning predictive-modeling tuning-parameters stacking

mlens - ML-Ensemble – high performance ensemble learning

•    Python

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ML-Ensemble combines a Scikit-learn high-level API with a low-level computational graph framework to build memory efficient, maximally parallelized ensemble networks in as few lines of codes as possible. ML-Ensemble is thread safe as long as base learners are and can fall back on memory mapped multiprocessing for memory-neutral process-based concurrency. For tutorials and full documentation, visit the project website.

ensemble-learning machine-learning ensemble learners stacking stack ensembles

heamy - A set of useful tools for competitive data science.

•    Python

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A set of useful tools for competitive data science.

machine-learning data-science stacking

StickyStack

•    Javascript

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A jQuery plugin that creates a stacking effect by sticking panels as they reach the top of the viewport. First include jQuery, then call .stickyStack() on the main content wrapper (or define it using options). Note that the stackingElements should be direct children of the containerElement.

viewport jquery-plugin stacking-panels stacking sticking-panels scroll-effect scroll-effects responsive

AutoMLPipeline

•    Julia

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AutoMLPipeline is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, manipulate pipeline expressions, and makes it easy to discover optimal structures for machine learning regression and classification. Just take note that + has higher priority than |> so if you are not sure, enclose the operations inside parentheses.

data-science machine-learning data-mining pipeline julia classification ensemble-learning data-mining-algorithms symbolic-expressions automl stacking chaining machine-learning-models pipeline-optimization pipeline-structure scikitlearn-wrapper symbolic-pipeline

DC-hi_guides - [Data Castle 算法竞赛] 精品旅行服务成单预测 final rank 11

•    Python

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[Data Castle 算法竞赛] 精品旅行服务成单预测 final rank 11

data-mining xgboost lightgbm stacking

xam - :dart: Personal data science and machine learning toolbox

•    Python

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xam is my personal data science and machine learning toolbox. It is written in Python 3 and stands on the shoulders of giants (mainly pandas and scikit-learn). It loosely follows scikit-learn's fit/transform/predict convention. ⚠️ Because xam is a personal toolkit, the --upgrade flag will install the latest releases of each dependency (scipy, pandas etc.). I like to stay up-to-date with the latest library versions.

machine-learning data-science preprocessing stacking

explorer - Explore transactions and accounts on the Stacks blockchain

•    TypeScript

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The Stacks Explorer is built with react, next.js and @stacks/ui. To run the explorer locally, you can clone this repo and install the dependencies needed. Make sure you have yarn installed. To build and run the application, you can run this yarn task which will launch the application at http://localhost:3000.

react typescript bitcoin nextjs blockchain emotion web3 blockstack stacking styled-system stacks-explorer stacks-blockchain