learning_rate: Controls the step size at each iteration while moving toward a minimum of the loss function. A smaller learning rate requires more trees but can lead to better performance.
num_leaves: The number of leaves in one tree. Increasing this value makes the model more complex and more likely to overfit.
feature_fraction: The fraction of features to be used for fitting the individual base learners. It helps prevent overfitting.
bagging_fraction: The fraction of data to be used for fitting the individual base learners.
max_depth: The maximum depth of a tree. Increasing this value makes the model more complex and more likely to overfit.
min_data_in_leaf: The minimum number of data points required in a leaf. It helps prevent overfitting.