gbt is a library for gradient boosted trees with minimal coding required. It is a thin wrapper around lightgbm. Give it a pandas.Dataframe, gbt.train() takes care of feature transforms (e.g. scaling for numerical features, label encoding for categorical features) and metrics print outs.
Install
pip install gbt
Quickstart
class DatasetBuilder:
def training_dataset(self):
df = pd.DataFrame(
{
"a": [1, 2, 3, 4, 5, 6, 7],
"b": ["a", "b", "c", None, "e", "f", "g"],
"c": [1, 0, 1, 1, 0, 0, 1],
"some_other_column": [0, 0, None, None, None, 3, 3],
}
)
return df
def testing_dataset(self):
return self.training_dataset()
TrainingPipeline(
params_preset="binary", # one of mape, l2, binary, multiclass
params_override={"num_leaves": 10},
label_column="c",
val_size=0.2, # fraction of the validation split
categorical_feature_columns=["b"],
numerical_feature_columns=["a"],
).fit(DatasetBuilder())
