# 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 unshown values in sparse matrices (and LightSVM) are treated as zeros. - When
`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.
- 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. - 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.

## LambdaRank¶

- 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
`max_position`

to set the NDCG optimization position.

## Parameters Tuning¶

- Refer to Parameters Tuning.

## Parallel Learning¶

- Refer to Parallel Learning Guide.

## GPU Support¶

- Refer to GPU Tutorial and GPU Targets.