Multi model cross validation | teradataml open-source ML functions - Multi model cross validation - Teradata Package for Python

Teradata® Package for Python User Guide

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Teradata Package for Python
Release Number
20.00
Published
December 2024
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en-US
ft:lastEdition
2025-01-23
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nvi1706202040305.ditamap
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rkb1531260709148
Product Category
Teradata Vantage

The following example shows multi model cross validation using td_lightgbm.

The argument return_cvbooster is not supported yet for cv().
>>> opt_cv_m = td_lightgbm.cv(params={}, train_set=obj_m_v, num_boost_round=30)
>>> opt_cv_m
	partition_column_1	partition_column_2												model									   console_output
0	                 1	                11	{'l2-mean': [0.2228372778767334, 0.20378581597...	[LightGBM] [Warning] Auto-choosing col-wise mu...
1	                 0	                11	{'l2-mean': [0.22670871234512563, 0.2076397899...	[LightGBM] [Warning] Auto-choosing col-wise mu...
2	                 1	                10	{'l2-mean': [0.2254886102590233, 0.20486831815...	[LightGBM] [Warning] Auto-choosing col-wise mu...
3	                 0	                10	{'l2-mean': [0.22325269845549206, 0.2001155354...	[LightGBM] [Warning] Auto-choosing col-wise mu...