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How does LightGBM deal with categorical features?
LightGBM can handle categorical features by taking the input of feature names. It 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.
Can LightGBM deal with missing values?
LightGBM enables the missing value handle by default. 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.
Can GBM handle categorical variables?
The XGBoost implementation of GBM does not handle categorical features natively because it did not have to.
How does CatBoost deal with categorical variables?
Handling Categorical features automatically: We can use CatBoost without any explicit pre-processing to convert categories into numbers. CatBoost converts categorical values into numbers using various statistics on combinations of categorical features and combinations of categorical and numerical features.
Does XGBoost handle categorical data?
Unlike CatBoost or LGBM, XGBoost cannot handle categorical features by itself, it only accepts numerical values similar to Random Forest. Therefore one has to perform various encodings like label encoding, mean encoding or one-hot encoding before supplying categorical data to XGBoost.
Why does’categorical feature’of LightGBM not work?
The problem is that lightgbm can handle only features, that are of category type, not object. Here the list of all possible categorical features is extracted. Such features are encoded into integers in the code. But nothing happens to objects and thus lightgbm complains, when it finds that not all features have been transformed into numbers.
When to use zero as missing in LightGBM?
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. LightGBM offers good accuracy with integer-encoded categorical features.
What are the parameters of a dataset in LightGBM?
Dataset Parameters ¶ 1 max number of bins that feature values will be bucketed in 2 small number of bins may reduce training accuracy but may increase general power (deal with over-fitting) 3 LightGBM will auto compress memory according to max_bin. For example, LightGBM will use uint8_t for feature value if max_bin=255
How to disable the Nan function in LightGBM?
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.