CXXGraph
xgboost
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| CXXGraph | xgboost | |
|---|---|---|
| 84 | 10 | |
| 393 | 25,548 | |
| - | 0.9% | |
| 8.5 | 9.6 | |
| 3 days ago | 7 days ago | |
| C++ | C++ | |
| GNU Affero General Public License v3.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
CXXGraph
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Hacktoberfest is ON CXXGraph
Actual Web Site
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Revolutionizing Data Processing with CXXGraph: A Comprehensive Guide to Graph Data Structures in C++
CXXGraph is a C++ library for graph data structures that provides an easy-to-use interface for creating and processing direct and nondirect graphs. CXXGraph provides a simplified interface for creating and manipulating graphs. Using CXXGraph, developers can create graphs, add and remove edges, and perform various graph processing algorithms.
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CXXGraph Library : Header-Only C++ Library for Graph Representation and Algorithms
If you have 5 minutes to get in touch, click on the project or write to me at [email protected]
- 2 Weeks of Hacktoberfest, How is it going??
- Hacktoberfest is started, give your contribution!
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Hacktobefest 2022: My Repo
View on GitHub
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GraphScope VS CXXGraph - a user suggested alternative
2 projects | 17 Mar 2022
A Fast Graph library in C++
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libgrape-lite VS CXXGraph - a user suggested alternative
2 projects | 17 Mar 2022
A good library for graph algorithms
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euler VS CXXGraph - a user suggested alternative
2 projects | 17 Mar 2022
Good alternative for algorithms
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xgboost VS CXXGraph - a user suggested alternative
2 projects | 28 Feb 2022
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?
sirix - SirixDB is an an embeddable, bitemporal, append-only database system and event store, storing immutable lightweight snapshots. It keeps the full history of each resource. Every commit stores a space-efficient snapshot through structural sharing. It is log-structured and never overwrites data. SirixDB uses a novel page-level versioning approach.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
graphlite - A lightweight C++ graph library
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
shields - Concise, consistent, and legible badges in SVG and raster format
tensorflow - An Open Source Machine Learning Framework for Everyone
bitcart - https://bitcart.ai
Keras - Deep Learning for humans
benchmark - A microbenchmark support library
mlpack - mlpack: a fast, header-only C++ machine learning library
Google Test - GoogleTest - Google Testing and Mocking Framework
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.