Discussing the article: "Data Science and ML (Part 23): Why LightGBM and XGBoost outperform a lot of AI models?"

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Check out the new article: Data Science and ML (Part 23): Why LightGBM and XGBoost outperform a lot of AI models?.

These advanced gradient-=boosted decision tree techniques offer superior performance and flexibility, making them ideal for financial modeling and algorithmic trading. Learn how to leverage these tools to optimize your trading strategies, improve predictive accuracy, and gain a competitive edge in the financial markets.

Gradient Boosted Decision Trees (GBDT) are a powerful machine learning technique used primarily for regression and classification tasks. They combine the predictions of multiple weak learners, usually decision trees, to create a strong predictive model.

The core idea is to build models sequentially, each new model attempting to correct the errors made by the previous ones.

These boosted trees such as:

  • Extreme Gradient Boosting (XGBoost): Which is a popular and efficient implementation of gradient boosting,
  • Light Gradient Boosting Machnine (LightGBM): Which was designed for high performance and efficiency, especially with large datasets.
  • CatBoost: Which Handles categorical features automatically and is robust against overfitting.

Have gained much popularity in the machine learning community as the algorithms of choice for many winning teams in machine learning competitions. In this article, we are going to discover how we can use these accurate models in our trading applications.


Author: Omega J Msigwa

 
Excellent article. I am going to try and replicate this work.
 
You have to crank it up to the cut-off :) CatBoost is somewhat superior to the others, according to benchmarks.
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