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Xgboost Alternatives
Similar projects and alternatives to xgboost
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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
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InfluxDB
InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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Prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
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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.
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H2O
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
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SaaSHub
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better xgboost alternative or higher similarity.
xgboost discussion
xgboost reviews and mentions
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 2024-10-29.
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What AI/ML Models Should You Use and Why?
Boosting Boosting is not a separate ML model but a technique that combines multiple weak learners to create a single model that can generate highly accurate predictions. Xgboost is a common boosting model that supports distributed training, resulting in faster training. According to research by Intel, Xgboost can be more effective than a neural network-based approach for tabular data. In addition, Xgboost is faster to train and doesn’t require as much data as neural networks need.
- XGBoost: The Scalable and Distributed Gradient Boosting Library
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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.
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A note from our sponsor - SaaSHub
www.saashub.com | 21 May 2025
Stats
Basic xgboost repo stats
13
26,922
9.7
6 days ago
dmlc/xgboost is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of xgboost is C++.
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