vowpal_wabbit
xgboost
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vowpal_wabbit | xgboost | |
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11 | 10 | |
8,398 | 25,528 | |
0.2% | 0.8% | |
8.3 | 9.7 | |
6 days ago | 6 days ago | |
C++ | C++ | |
GNU General Public License v3.0 or later | 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.
vowpal_wabbit
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Microsoft Reinforcement Learning Open Source Fest 2022 – Native CSV Parser
My project here at the Reinforcement Learning Open Source Fest 2022 is to add the native CSV parsing feature for the Vowpal Wabbit.
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Predicting numerical values to a very high accuracy
If you only have 198 possible values then extreme multiclass models might benefit here with better precision and faster convergence. For example probabilistic label trees might have some relevance. Vowpal Wabbit also has specific reductions for extreme multi class problems. Might be worth a try if other alternatives still don't work out.
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Performance comparison: counting words in Python, Go, C++, C, AWK, Forth, and Rust
You're likely correct, but I do recall attending a lecture by John Langford of https://vowpalwabbit.org/ running some form of an N-gram C++ based NLP model, including summary statistics on performance, in less time than wc -l took on the same data. Must have some neat hashing tricks, but still was cool
xgboost
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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 VS CXXGraph - a user suggested alternative
2 projects | 28 Feb 2022
What are some alternatives?
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.
tensorflow - An Open Source Machine Learning Framework for Everyone
Keras - Deep Learning for humans
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.
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
scikit-learn - scikit-learn: machine learning in Python
Simple GAN - Attempt at implementation of a simple GAN using Keras
MLflow - Open source platform for the machine learning lifecycle
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)