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该页面包含了 LightGBM 中所有的参数.


针对 Leaf-wise (最佳优先) 树的参数优化

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 convenge much faster. However, the leaf-wise growth may be over-fitting if not used with the appropriate parameters.

LightGBM 使用 leaf-wise 的树生长策略, 而很多其他流行的算法采用 depth-wise 的树生长策略. 与 depth-wise 的树生长策略相较, leaf-wise 算法可以收敛的更快. 但是, 如果参数选择不当的话, leaf-wise 算法有可能导致过拟合.

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

想要在使用 leaf-wise 算法时得到好的结果, 这里有几个重要的参数值得注意:

  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 convert from depth-wise tree. However, this simple conversion is not good in practice. The reason is, when number of leaves are the same, the leaf-wise tree is much deeper than depth-wise tree. As a result, it may be 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=6 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. Actually, the concept depth can be forgotten in leaf-wise tree, since it doesn’t have a correct mapping from leaves to depth.

  2. num_leaves. 这是控制树模型复杂度的主要参数. 理论上, 借鉴 depth-wise 树, 我们可以设置 num_leaves = 2^(max_depth) 但是, 这种简单的转化在实际应用中表现不佳. 这是因为, 当叶子数目相同时, leaf-wise 树要比 depth-wise 树深得多, 这就有可能导致过拟合. 因此, 当我们试着调整 num_leaves 的取值时, 应该让其小于 2^(max_depth). 举个例子, 当 max_depth=6 时(这里译者认为例子中, 树的最大深度应为7), depth-wise 树可以达到较高的准确率.但是如果设置 num_leaves127 时, 有可能会导致过拟合, 而将其设置为 7080 时可能会得到比 depth-wise 树更高的准确率. 其实, depth 的概念在 leaf-wise 树中并没有多大作用, 因为并不存在一个从 leavesdepth 的合理映射.

  3. min_data_in_leaf. This is a very important parameter to deal with over-fitting in leaf-wise tree. Its value depends on the number of training data 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.

  4. min_data_in_leaf. 这是处理 leaf-wise 树的过拟合问题中一个非常重要的参数. 它的值取决于训练数据的样本个树和 num_leaves. 将其设置的较大可以避免生成一个过深的树, 但有可能导致欠拟合. 实际应用中, 对于大数据集, 设置其为几百或几千就足够了.

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

  6. max_depth. 你也可以利用 max_depth 来显式地限制树的深度.


  • 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 并行学习指南
  • 通过设置 bagging_fractionbagging_freq 参数来使用 bagging 方法
  • 通过设置 feature_fraction 参数来使用特征的子抽样
  • 使用较小的 max_bin
  • 使用 save_binary 在未来的学习过程对数据加载进行加速
  • 使用并行学习, 可参考 并行学习指南


  • 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
  • 使用较大的 max_bin (学习速度可能变慢)
  • 使用较小的 learning_rate 和较大的 num_iterations
  • 使用较大的 num_leaves (可能导致过拟合)
  • 使用更大的训练数据
  • 尝试 dart


  • 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
  • 使用较小的 max_bin
  • 使用较小的 num_leaves
  • 使用 min_data_in_leafmin_sum_hessian_in_leaf
  • 通过设置 bagging_fractionbagging_freq 来使用 bagging
  • 通过设置 feature_fraction 来使用特征子抽样
  • 使用更大的训练数据
  • 使用 lambda_l1, lambda_l2min_gain_to_split 来使用正则
  • 尝试 max_depth 来避免生成过深的树