Which is better Random forest or XG Boost

Question

Accepted Answer

Both Random Forest and XG Boost are powerful machine learning algorithms, but their performance depends on the specific problem and data.

Random Forest is an ensemble learning method that combines multiple decision trees to make predictions.
It is known for its ability to handle high-dimensional data and maintain good performance even with noisy or missing data.
XG Boost, on the other hand, is a gradient boosting algorithm that focuses on improving the weaknesses of decision trees.
It uses a more advanced optimization technique and can often outperform Random Forest in terms of predictive accuracy.
However, XG Boost requires more careful tuning of hyperparameters and can be more computationally expensive.
The choice between Random Forest and XG Boost depends on the specific problem, data characteristics, and available computational resources.

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