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I am doing an xgboost model for landslides assessment and I am using max_depth as one of my hyperparameters, but I don't understand how does it affect model analysis if it was high or low

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When max_depth is set to a high value, the model becomes more complex and has the potential to capture intricate patterns in the training data. However, this can also lead to overfitting, and may also consume more memory during training.

Conversely, setting a lower value for max_depth results in a simpler model that is less likely to overfit. This can lead to faster training times and may require less memory. However, a model with a low max_depth may not capture complex relationships in the data, potentially leading to underfitting.

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