Missing Value Handle¶
- LightGBM enables the missing value handle by default. Disable it by setting
- LightGBM uses NA (NaN) to represent missing values by default. Change it to use zero by setting
zero_as_missing=false(default), the unshown values in sparse matrices (and LightSVM) are treated as zeros.
zero_as_missing=true, NA and zeros (including unshown 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.
categorical_featureto specify the categorical features. Refer to the parameter
- 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.
cat_smoothto deal with over-fitting (when
#datais small or
- For a categorical feature with high cardinality (
#categoryis 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).
label_gainto set the gain(weight) of
max_positionto set the NDCG optimization position.