Advanced Topics

Missing Value Handle

  • LightGBM enables the missing value handle by default. Disable it by setting use_missing=false.

  • LightGBM uses NA (NaN) to represent missing values by default. Change it to use zero by setting zero_as_missing=true.

  • When zero_as_missing=false (default), the unrecorded values in sparse matrices (and LightSVM) are treated as zeros.

  • When zero_as_missing=true, NA and zeros (including unrecorded values in sparse matrices (and LightSVM)) are treated as missing.

Categorical Feature Support

  • LightGBM offers good accuracy with integer-encoded categorical features. LightGBM applies Fisher (1958) to find the optimal split over categories as described here. This often performs better than one-hot encoding.

  • Use categorical_feature to specify the categorical features. Refer to the parameter categorical_feature in Parameters.

  • Categorical features must be encoded as non-negative integers (int) less than Int32.MaxValue (2147483647). It is best to use a contiguous range of integers started from zero.

  • Use min_data_per_group, cat_smooth to deal with over-fitting (when #data is small or #category is large).

  • For a categorical feature with high cardinality (#category is large), it often works best to treat the feature as numeric, either by simply ignoring the categorical interpretation of the integers or by embedding the categories in a low-dimensional numeric space.


  • The label should be of type int, such that larger numbers correspond to higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect).

  • Use label_gain to set the gain(weight) of int label.

  • Use lambdarank_truncation_level to truncate the max DCG.

Cost Efficient Gradient Boosting

Cost Efficient Gradient Boosting (CEGB) makes it possible to penalise boosting based on the cost of obtaining feature values. CEGB penalises learning in the following ways:

  • Each time a tree is split, a penalty of cegb_penalty_split is applied.

  • When a feature is used for the first time, cegb_penalty_feature_coupled is applied. This penalty can be different for each feature and should be specified as one double per feature.

  • When a feature is used for the first time for a data row, cegb_penalty_feature_lazy is applied. Like cegb_penalty_feature_coupled, this penalty is specified as one double per feature.

Each of the penalties above is scaled by cegb_tradeoff. Using this parameter, it is possible to change the overall strength of the CEGB penalties by changing only one parameter.

Parameters Tuning

Distributed Learning

GPU Support

Recommendations for gcc Users (MinGW, *nix)