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CatBoost VS MXNet

Compare CatBoost VS MXNet and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

CatBoost logo CatBoost

CatBoost - state-of-the-art open-source gradient boosting library with categorical features support, https://catboost.yandex/ #catboost

MXNet logo MXNet

MXNet is a deep learning framework.
  • CatBoost Landing page
    Landing page //
    2021-10-16
  • MXNet Landing page
    Landing page //
    2022-07-25

CatBoost videos

+ Add

[Paper Review]Catboost: Unbiased Boosting with Categorical Features

More videos:

  • Review - 04-9: Ensemble Learning - CatBoost (앙상블 기법 - CatBoost)
  • Review - Free Udemy Course - CatBoost vs XGBoost - Classification and Regression Modeling with Python

MXNet videos

+ Add

Apache MXNet 2.0: Bridging Deep Learning and Machine Learning

More videos:

  • Review - MXNet Introduction: MXNet Vancouver Meetup
  • Review - Extending Apache MXNet for new features and performance

Category Popularity

0-100% (relative to CatBoost and MXNet)

CatBoost

MXNet

Data Science And Machine Learning
41 41%
Data Science And Machine Learning
59% 59
Data Science Tools
59 59%
Data Science Tools
41% 41
Machine Learning
54 54%
Machine Learning
46% 46
Business & Commerce
0 0%
Business & Commerce
100% 100

User comments

Share your experience with using CatBoost and MXNet. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, CatBoost seems to be more popular. It has been mentiond 4 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

CatBoost mentions (4)

  • What's New with AWS: Amazon SageMaker built-in algorithms now provides four new Tabular Data Modeling Algorithms
    CatBoost is another popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT). To learn how to use this algorithm, please see example notebooks for Classification and Regression. - Source: dev.to / almost 2 years ago
  • Writing the fastest GBDT libary in Rust
    Here are our benchmarks on training time comparing Tangram's Gradient Boosted Decision Tree Library to LightGBM, XGBoost, CatBoost, and sklearn. - Source: dev.to / over 2 years ago
  • Data Science toolset summary from 2021
    Catboost - CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which attempts to solve for Categorical features using a permutation driven alternative compared to the classical algorithm. Link - https://catboost.ai/. - Source: dev.to / over 2 years ago
  • CatBoost Quickstart — ML Classification
    CatBoost is an open source algorithm based on gradient boosted decision trees. It supports numerical, categorical and text features. Check out the docs. - Source: dev.to / about 3 years ago

MXNet mentions (0)

We have not tracked any mentions of MXNet yet. Tracking of MXNet recommendations started around Mar 2021.

What are some alternatives?

When comparing CatBoost and MXNet, you can also consider the following products

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Open Text Magellan - OpenText Magellan - the power of AI in a pre-wired platform that augments decision making and accelerates your business. Learn more.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Kira - Gain visibility into contract repositories, accelerate and improve the accuracy of contract review, mitigate risk of errors, win new business, and improve the value you provide to your clients.

Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

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