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  1. Stackups
  2. DevOps
  3. Build Automation
  4. Python Build Tools
  5. PyBrain vs XGBoost

PyBrain vs XGBoost

OverviewComparisonAlternatives

Overview

XGBoost
XGBoost
Stacks192
Followers86
Votes0
GitHub Stars27.6K
Forks8.8K
PyBrain
PyBrain
Stacks0
Followers6
Votes0

PyBrain vs XGBoost: What are the differences?

Developers describe PyBrain as "A modular Machine Learning Library for Python". It's goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. On the other hand, XGBoost is detailed as "Scalable and Flexible Gradient Boosting". 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, Flink and DataFlow.

PyBrain and XGBoost are primarily classified as "Machine Learning" and "Python Build" tools respectively.

Some of the features offered by PyBrain are:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

On the other hand, XGBoost provides the following key features:

  • Flexible
  • Portable
  • Multiple Languages

XGBoost is an open source tool with 19.7K GitHub stars and 7.72K GitHub forks. Here's a link to XGBoost's open source repository on GitHub.

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Detailed Comparison

XGBoost
XGBoost
PyBrain
PyBrain

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, Flink and DataFlow

It's goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.

Flexible; Portable; Multiple Languages; Battle-tested
Supervised Learning; Unsupervised Learning; Reinforcement Learning; Black-box Optimization; Network Architectures; Toy Environments; 3D Environments; Function Environments; Pole-Balancing
Statistics
GitHub Stars
27.6K
GitHub Stars
-
GitHub Forks
8.8K
GitHub Forks
-
Stacks
192
Stacks
0
Followers
86
Followers
6
Votes
0
Votes
0
Integrations
Python
Python
C++
C++
Java
Java
Scala
Scala
Julia
Julia
Python
Python

What are some alternatives to XGBoost, PyBrain?

TensorFlow

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

scikit-learn

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

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