Data Flow Facilitator for Machine Learning (dffml)
The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease. (by intel)
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 (by dmlc)
| Data Flow Facilitator for Machine Learning (dffml) | xgboost | |
|---|---|---|
| - | 11 | |
| 241 | 25,595 | |
| 0.0% | 0.5% | |
| 9.1 | 9.6 | |
| 5 days ago | 7 days ago | |
| Python | C++ | |
| MIT License | 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.
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.
Data Flow Facilitator for Machine Learning (dffml)
Posts with mentions or reviews of Data Flow Facilitator for Machine Learning (dffml).
We have used some of these posts to build our list of alternatives
and similar projects.
We haven't tracked posts mentioning Data Flow Facilitator for Machine Learning (dffml) yet.
Tracking mentions began in Dec 2020.
xgboost
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 2024-05-05.
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stackgbm VS xgboost - a user suggested alternative
2 projects | 5 May 2024
- 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
What are some alternatives?
When comparing Data Flow Facilitator for Machine Learning (dffml) and xgboost you can also consider the following projects:
gym - A toolkit for developing and comparing reinforcement learning algorithms.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
scikit-learn - scikit-learn: machine learning in Python
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
tensorflow - An Open Source Machine Learning Framework for Everyone
Keras - Deep Learning for humans
mlpack - mlpack: a fast, header-only C++ machine learning library
MindsDB - The platform for customizing AI from enterprise data
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.
Data Flow Facilitator for Machine Learning (dffml) vs gym
xgboost vs Prophet
Data Flow Facilitator for Machine Learning (dffml) vs scikit-learn
xgboost vs MLP Classifier
Data Flow Facilitator for Machine Learning (dffml) vs tensorflow
xgboost vs tensorflow
Data Flow Facilitator for Machine Learning (dffml) vs Prophet
xgboost vs Keras
Data Flow Facilitator for Machine Learning (dffml) vs Keras
xgboost vs mlpack
Data Flow Facilitator for Machine Learning (dffml) vs MindsDB
xgboost vs catboost