Parameters Tuning

This page contains parameters tuning guides for different scenarios.

List of other helpful links

Tune Parameters for the Leaf-wise (Best-first) Tree

LightGBM uses the leaf-wise tree growth algorithm, while many other popular tools use depth-wise tree growth. Compared with depth-wise growth, the leaf-wise algorithm can converge much faster. However, the leaf-wise growth may be over-fitting if not used with the appropriate parameters.

To get good results using a leaf-wise tree, these are some important parameters:

  1. num_leaves. This is the main parameter to control the complexity of the tree model. Theoretically, we can set num_leaves = 2^(max_depth) to obtain the same number of leaves as depth-wise tree. However, this simple conversion is not good in practice. The reason is that a leaf-wise tree is typically much deeper than a depth-wise tree for a fixed number of leaves. Unconstrained depth can induce over-fitting. Thus, when trying to tune the num_leaves, we should let it be smaller than 2^(max_depth). For example, when the max_depth=7 the depth-wise tree can get good accuracy, but setting num_leaves to 127 may cause over-fitting, and setting it to 70 or 80 may get better accuracy than depth-wise.

  2. min_data_in_leaf. This is a very important parameter to prevent over-fitting in a leaf-wise tree. Its optimal value depends on the number of training samples and num_leaves. Setting it to a large value can avoid growing too deep a tree, but may cause under-fitting. In practice, setting it to hundreds or thousands is enough for a large dataset.

  3. max_depth. You also can use max_depth to limit the tree depth explicitly.

For Faster Speed

  • Use bagging by setting bagging_fraction and bagging_freq

  • Use feature sub-sampling by setting feature_fraction

  • Use small max_bin

  • Use save_binary to speed up data loading in future learning

  • Use parallel learning, refer to Parallel Learning Guide

For Better Accuracy

  • Use large max_bin (may be slower)

  • Use small learning_rate with large num_iterations

  • Use large num_leaves (may cause over-fitting)

  • Use bigger training data

  • Try dart

Deal with Over-fitting

  • Use small max_bin

  • Use small num_leaves

  • Use min_data_in_leaf and min_sum_hessian_in_leaf

  • Use bagging by set bagging_fraction and bagging_freq

  • Use feature sub-sampling by set feature_fraction

  • Use bigger training data

  • Try lambda_l1, lambda_l2 and min_gain_to_split for regularization

  • Try max_depth to avoid growing deep tree

  • Try extra_trees

  • Try increasing path_smooth