HotBits Python API VS xgboost

HotBits Python API

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xgboost

• What AI/ML Models Should You Use and Why?
  3 projects | dev.to | 29 Oct 2024

  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
  1 project | news.ycombinator.com | 14 Aug 2024
• stackgbm VS xgboost - a user suggested alternative
  2 projects | 5 May 2024
• XGBoost 2.0
  1 project | news.ycombinator.com | 13 Oct 2023
• XGBoost2.0
  1 project | news.ycombinator.com | 9 Oct 2023
• Xgboost: Banding continuous variables vs keeping raw data
  1 project | /r/datascience | 1 Jun 2023
• PSA: You don't need fancy stuff to do good work.
  10 projects | /r/datascience | 9 May 2023

  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.

• XGBoost Save and Load Error
  1 project | /r/datascience | 14 Nov 2022

  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.

• For XGBoost (in Amazon SageMaker), one of the hyper parameters is num_round, for number of rounds to train. Does this mean cross validation?
  1 project | /r/learnmachinelearning | 23 Sep 2022

  Reference: https://github.com/dmlc/xgboost/issues/2031

• CS Internship Questions
  1 project | /r/stanford | 7 May 2022

  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.