xgboost VS Keras

Compare xgboost vs Keras and see what are their differences.


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 (by dmlc)
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xgboost Keras
6 65
23,911 57,755
0.5% 0.6%
9.1 9.2
2 days ago 3 days ago
C++ Python
Apache License 2.0 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.


Posts with mentions or reviews of xgboost. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-02-28.


Posts with mentions or reviews of Keras. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-02-01.
  • How to query pandas DataFrames with SQL
    5 projects | dev.to | 1 Feb 2023
    Pandas comes with many complex tabular data operations. And, since it exists in a Python environment, it can be coupled with lots of other powerful libraries, such as Requests (for connecting to other APIs), Matplotlib (for plotting data), Keras (for training machine learning models), and many more.
  • The Essentials of a Contributor-friendly Open-source Project
    2 projects | dev.to | 18 Jan 2023
    Our trick is to support GitHub Codespaces, which provides a web-based Visual Studio Code IDE. The best thing is you can specify a Dockerfile with all the required dependency software installed. With one click on the repo’s webpage, your contributors are ready to code. Here is our setup for your reference.
    7 projects | dev.to | 16 Jan 2023
    If you’re looking for further resources on running TensorFlow and Keras on a newer MacBook, I recommend checking out this YouTube video: How to Install Keras GPU for Mac M1/M2 with Conda
  • Doing k-fold analysis
    2 projects | reddit.com/r/tensorflow | 25 Dec 2022
    The thing that pops right into my mind is the following issue: https://github.com/keras-team/keras/issues/13118 People are still reporting memory leaks when calling model.predict and I wouldn't be surprised if model.fit also leaked when called multiple times. Maybe this is a good starting point for your investigation. If this is unrelated, I'm sorry in forward.
  • 65 Blog Posts to Learn Data Science
    2 projects | dev.to | 30 Nov 2022
    Hello world. This tutorial is a gentle introduction to building modern text recognition system using deep learning in 15 minutes. It will teach you the main ideas of how to use Keras and Supervisely for this problem. This guide is for anyone who is interested in using Deep Learning for text recognition in images but has no idea where to start.
  • Инструменты Python. Библиотеки для анализа данных
    7 projects | reddit.com/r/u_DenoiseLAB | 10 Nov 2022
    - statsmodel (https://keras.io);
  • Keras vs Tensorflow vs Pytorch for a Final year Project
    2 projects | reddit.com/r/tensorflow | 10 Oct 2022
    E.g. If you consider it image classification (you already have the pedestrians extracted and just need to classify their intent), you might find that easier to do with Keras, just butcher one of the examples on keras.io. You might also find fast.ai more to your liking.
  • A few (unordered) thoughts about data (1/2)
    6 projects | dev.to | 5 Oct 2022
  • How to Build a Machine Learning Recommendation Engine w/ TensorFlow & HarperDB
    3 projects | dev.to | 24 Aug 2022
    This is where machine learning takes over. Using libraries such as TensorFlow Recommenders with Keras models, it's easy to shape the data in ways that will allow the items and users to be viewed and compared in a multidimensional perspective. Qualitative features such as item categories and user profile attributes can be mapped into mathematical concepts that can be quantitatively compared with one another, ultimately providing new insights and better recommendations.
  • Beginner Friendly Resources to Master Artificial Intelligence and Machine Learning with Python (2022)
    8 projects | dev.to | 14 Aug 2022
    Keras – An open-source software library that provides a Python interface to TensorFlow for artificial neural networks

What are some alternatives?

When comparing xgboost and Keras you can also consider the following projects:

MLP Classifier - A handwritten multilayer perceptron classifer using numpy.

scikit-learn - scikit-learn: machine learning in Python

Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

tensorflow - An Open Source Machine Learning Framework for Everyone

Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

TFLearn - Deep learning library featuring a higher-level API for TensorFlow.

pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]

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.

mlpack - mlpack: a fast, header-only C++ machine learning library