OpenHotspot VS catboost

Compare OpenHotspot vs catboost and see what are their differences.

OpenHotspot

By ozylol

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. (by catboost)
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OpenHotspot catboost
0 4
14 6,233
- 1.2%
0.0 9.5
over 4 years ago 6 days ago
C++ C
- Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

OpenHotspot

Posts with mentions or reviews of OpenHotspot. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning OpenHotspot yet.
Tracking mentions began in Dec 2020.

catboost

Posts with mentions or reviews of catboost. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-13.
  • Data Science toolset summary from 2021
    13 projects | dev.to | 13 Nov 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/
  • CatBoost Quickstart — ML Classification
    2 projects | dev.to | 15 Mar 2021
    CatBoost is an open source algorithm based on gradient boosted decision trees. It supports numerical, categorical and text features. Check out the docs.
  • [D] What are your favorite Random Forest implementations that support categoricals
    2 projects | reddit.com/r/MachineLearning | 20 Feb 2021
    If you considering GBDT check out catboost, unfortunately RF mode is not available but library implement lots of interesting categorical encoding tricks that boost accuracy.
  • CatBoost and Water Pumps
    2 projects | dev.to | 20 Feb 2021
    The data contains a large number of categorical features. The most suitable for obtaining a base-line model, in my opinion, is CatBoost. It is a high-performance, open-source library for gradient boosting on decision trees.

What are some alternatives?

When comparing OpenHotspot and catboost you can also consider the following projects:

xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

Recommender - A C library for product recommendations/suggestions using collaborative filtering (CF)

Porcupine   - On-device wake word detection powered by deep learning.

mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

vowpal_wabbit - Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

mlpack - mlpack: a scalable C++ machine learning library --

faiss-server - faiss serving :)

NN++ - A small and easy to use neural net implementation for C++. Just download and #include!

Caffe - Caffe: a fast open framework for deep learning.

Dlib - A toolkit for making real world machine learning and data analysis applications in C++