Mljar-supervised Alternatives

Similar projects and alternatives to mljar-supervised

  • GitHub repo mljar-examples

    Examples how MLJAR can be used

  • GitHub repo Prophet

    Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

  • GitHub repo LightGBM

    A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

  • GitHub repo autokeras

    AutoML library for deep learning

  • GitHub repo optuna

    A hyperparameter optimization framework

  • GitHub repo pyGAM

    [HELP REQUESTED] Generalized Additive Models in Python

  • GitHub repo soda-sql

    Data profiling, testing, and monitoring for SQL accessible data.

  • GitHub repo automlbenchmark

    OpenML AutoML Benchmarking Framework

  • GitHub repo Empirical_Study_of_Ensemble_Learning_Methods

    Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning

  • GitHub repo python-project-template

    A template for python projects

NOTE: The number of mentions on this list indicates mentions on common posts. Hence, a higher number means a better mljar-supervised alternative or higher similarity.

Posts

Posts where mljar-supervised has been mentioned. We have used some of these posts to build our list of alternatives and similar projects - the last one was on 2021-06-20.
  • I want to enter into the data science, so was wondering is there anyone who is doing a project i can help with (not sure if that is allowed) to gain some experience?
    Im working on open source AutoML project https://github.com/mljar/mljar-supervised It is in python and work with tabular data. In my opinion contributing to open source projects is valuable experience. If interested I can point some issues to start.
  • What are some exciting new tools/libraries in 2021?
    MLJAR AutoML for tabular data - https://github.com/mljar/mljar-supervised - it has automatic documentation for created models
  • Fixed my problem earlier, now I'm getting a new error.
    I'm the main contributor to MLJAR AutoML (automated machine learning), and you can use AutoML to get results from different algorithms without worrying about proper data preparation (AutoML will handle this). The link to repo https://github.com/mljar/mljar-supervised MLJAR will produce for you documentation for each model.
  • Open-source work in data science for a newbie.
    I'm working on an open-source AutoML tool. If you want to learn and help I can think about some issues good for start for you (maybe you will come with some new ideas). I have a list of issues with help needed tag - but they can be hard at the beginning. Creating examples/tutorials or improving the docs are also welcome. Anyway, if interested, please let me know, for sure we will come with something interesting and valuable to start & learn.
  • What is the best classifier method?
    I'm working on AutoML tool called MLJAR, available at github https://github.com/mljar/mljar-supervised you can use it to quickly check many different algorithms and easy ensemble them. It is open-source :)
  • Is it possible to clean memory after using a package that has a memory leak in my python script?
    reddit.com/r/Python | 2021-04-29
    I'm working on the AutoML python package (Github repo). In my package, I'm using many different algorithms. One of the algorithms is LightGBM. The algorithm after the training doesn't release the memory, even if del is called and gc.collect() after. I created the issue on LightGBM GitHub -> link. Because of this leak, memory consumption is growing very fast during algorithm training.
  • Easily add Machine Learning to your data pipeline with MLJAR AutoML an open-source (MIT) python package for tabular data
  • What's your best use of AutoML?
    In the MLJAR AutoML (https://github.com/mljar/mljar-supervised) you have mode Explain which is designed to explain data with ML. With this mode you will get a lot of explanations for your data: SHAP plots, decision tree visualization, decision rules in text format, feature importance. If you run the AutoML in Compete mode the Golden Features will be searched and constructed, maybe you will find some new features that have meaning for the business. In case of any questions, I'm happy to help!
    I've just found article that summarizes the ML hackathon where the second-place winner uses autoML tools! The 2nd place winner used AutoViML and MLJAR - I'm the main contributor to MLJAR AutoML https://github.com/mljar/mljar-supervised and I was super happy to see that someone using AutoML can score so high in ML competition
    Yes, you will be define any validation you want. Right now, I'm working on a custom validation strategy https://github.com/mljar/mljar-supervised/issues/380
  • Hyperparameter optimization returning different optimal architectures
    You can try MLJAR AutoML https://github.com/mljar/mljar-supervised I belive it will be more suitable for your task. It will do NN architecture search but it searches for simple architectures (2 layers only). For your problem gradinent boosting methods might work pretty well, like xgboost, lightgbm, catboost (all available in MLJAR).
  • ML Libs robust to missing data?
    AutoML can handle data with missing values. I can recommend python-package MLJAR AutoML https://github.com/mljar/mljar-supervised - I'm the main contributor to the package.
  • [D] [P] How do you use tools like AutoML?
    In case you would like to check, my AutoML: https://github.com/mljar/mljar-supervised :)
  • AutoML / Model for multi-output regression or deep learning
    I'm working on AutoML tool called MLJAR, it is open-source: https://github.com/mljar/mljar-supervised and we only support only one-output regression ...
  • MLJAR Automated Machine Learning Python Package for classification and regression on tabular data

Stats

Basic mljar-supervised repo stats
37
1,406
9.5
11 days ago

mljar/mljar-supervised is an open source project licensed under MIT License which is an OSI approved license.