Data preparation | Model Operations using td_lightgbm | teradataml OpenSourceML - Data preparation - Teradata VantageCloud Lake

Lake - Analyze Your Data with ClearScape Analytics™
Deployment
VantageCloud
Edition
Lake
Product
Teradata VantageCloud Lake
Release Number
Published
February 2025
ft:locale
en-US
ft:lastEdition
2026-02-20
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tcl1683670667798.ditamap
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pny1626732985837.ditaval
dita:id
tcl1683670667798

Use the relevant single or multi model case statement to prepare your data, then validate the dataset and get the pandas DataFrame of data.

Single model case

>>> obj_s = td_lightgbm.Dataset(df_x_classif, df_y_classif, silent=True, free_raw_data=False)

Multi model case

>>> obj_m = td_lightgbm.Dataset(df_x_classif, df_y_classif, free_raw_data=False,
                                partition_columns=["partition_column_1", "partition_column_2"])

Validation dataset

>>> obj_m_v = td_lightgbm.Dataset(df_x_classif, df_y_classif, free_raw_data=False,
                                  partition_columns=["partition_column_1", "partition_column_2"])

Get pandas DataFrame of data for training locally

>>> pdf_x = df_x_classif.to_pandas().reset_index()
>>> pdf_y = df_y_classif.to_pandas()