Can you explain the hyperparameters in the XGBoost algorithm?

Question

Accepted Answer

Hyperparameters in XGBoost algorithm control the behavior of the model during training.

Hyperparameters include parameters like learning rate, max depth, number of trees, etc.
They are set before the training process and can greatly impact the model's performance.
Example: 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100

Accepted Answer

Hyperparameters are non-learnable parameters, which are totally in our hands, how to tune these hyperparameters to attain the maximum accuracy of the model, in terms of XGboost,

these are the following hyperparameters:

depth of tree: how much depth should we keep to achieve an excellent result?
learning_rate: learning rate is also a major factor to learn the model because it updates the parameters, if is it too low then our model takes a long time to train if is it too high then it is not able to reach minima of a cost function which we are looking to minimize.
Sampling and column sample: we should also take care of the dataset while training the model , the low value is fine to prevent overfitting at each split and if the value becomes too small then it may under-fit the dataset.

regularization l1 and l2: these two regularization factors also play an important role in model efficiency they penalize the redundant features and help to prevent overfitting.

there are also other hyperparameters but these are more play an important role in the efficiency of model.

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