openskill.py
xgboost
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| openskill.py | xgboost | |
|---|---|---|
| 22 | 10 | |
| 241 | 25,576 | |
| 4.1% | 1.0% | |
| 7.3 | 9.6 | |
| 6 days ago | 4 days ago | |
| Jupyter Notebook | C++ | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
openskill.py
- Show HN: Predict team ranks in sports and video games with openskill.py
- Predict how teams will rank in sports/video games using our rating system.
- I made a project with the ability to predict ranks of teams in a sports/video game match.
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Openskill: A patent-free alternative to TrueSkill
Linked is the Javascript library. There are also ports of this to Python, Kotlin, Lua, and Elixir.
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Predicting Overwatch Match Outcomes with 90% Accuracy
The new benchmark code can be found here.
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openskill.py VS trueskill - a user suggested alternative
2 projects | 30 Jan 2022
- openskill.py can now predict the winners of any game with 90% accuracy and faster than TrueSkill
xgboost
- XGBoost 2.0
- XGBoost2.0
- Xgboost: Banding continuous variables vs keeping raw data
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PSA: You don't need fancy stuff to do good work.
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive documentation and community support, making it easy to learn and apply new techniques without needing specialized training or expensive software licenses.
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XGBoost Save and Load Error
You can find the problem outlined here: https://github.com/dmlc/xgboost/issues/5826. u/hcho3 diagnosed the problem and corrected it as of XGB version 1.2.0.
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For XGBoost (in Amazon SageMaker), one of the hyper parameters is num_round, for number of rounds to train. Does this mean cross validation?
Reference: https://github.com/dmlc/xgboost/issues/2031
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CS Internship Questions
By the way, most of the time XGBoost works just as well for projects, would not recommend applying deep learning to every single problem you come across, it's something Stanford CS really likes to showcase when it's well known (1) that sometimes "smaller"/less complex models can perform just as well or have their own interpretive advantages and (2) it is well known within ML and DS communities that deep learning does not perform as well with tabular datasets and using deep learning as a default to every problem is just poor practice. However, if you do (god forbid) get language, speech/audio, vision/imaging, or even time series models then deep learning as a baseline is not the worst idea.
- OOM with ML Models (SKlearn, XGBoost, etc), workaround/tips for large datasets?
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xgboost VS CXXGraph - a user suggested alternative
2 projects | 28 Feb 2022
- 'y contains previously unseen labels' (label encoder)
What are some alternatives?
trueskill - An implementation of the TrueSkill rating system for Python
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
karateclub - Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
tensorflow - An Open Source Machine Learning Framework for Everyone
seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
Keras - Deep Learning for humans
Data Flow Facilitator for Machine Learning (dffml) - The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease.
mlpack - mlpack: a fast, header-only C++ machine learning library
catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.