The following example shows a single model cross validation using td_lightgbm.
return_cvbooster argument is not yet supported for cv().
>>> opt_cv_s = td_lightgbm.cv(params={}, train_set=obj_s1, callbacks=[td_lightgbm.early_stopping(5)]) >>> opt_cv_s [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000058 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 532 [LightGBM] [Info] Number of data points in the train set: 320, number of used features: 4 [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000054 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 532 [LightGBM] [Info] Number of data points in the train set: 320, number of used features: 4 [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000051 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 532 [LightGBM] [Info] Number of data points in the train set: 320, number of used features: 4 [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000045 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 532 [LightGBM] [Info] Number of data points in the train set: 320, number of used features: 4 [LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000047 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 532 [LightGBM] [Info] Number of data points in the train set: 320, number of used features: 4 Training until validation scores don't improve for 5 rounds Early stopping, best iteration is: [57] cv_agg's l2: 0.067099 + 0.0151336 {'l2-mean': [0.22023353349592326, 0.19595424631042407, ... ... 0.06726610385135459, 0.06709902214997468], 'l2-stdv': [0.0015084316502001842, 0.0029052033405121175, ... ... 0.015130340427949404, 0.015133573776865996]}