| _Regressor model_ | | | |
| freqai.regressor | `xgboost` | enum {`xgboost`,`lightgbm`,`histgradientboostingregressor`,`ngboost`,`catboost`} | Machine learning regressor algorithm. |
| _Model training parameters_ | | | |
-| freqai.model_training_parameters.gpu_vram_gb | 80 | enum {8,10,12,16,24,32,40,48,64,80} | GPU VRAM capacity (GB) for CatBoost. Constrains `depth`, `border_count`, and `max_ctr_complexity` ranges to fit memory. |
+| freqai.model_training_parameters.gpu_vram_gb | 80 | enum {8,10,12,16,24,32,40,48,64,80} | Available GPU VRAM (GB) for CatBoost, not total. Constrains `depth`, `border_count`, and `max_ctr_complexity` ranges. |
| _Data split parameters_ | | | |
| freqai.data_split_parameters.method | `train_test_split` | enum {`train_test_split`,`timeseries_split`} | Data splitting strategy. `train_test_split` for sequential split, `timeseries_split` for chronological split with configurable gap. |
| freqai.data_split_parameters.test_size | 0.1 / None | float (0,1) \| int >= 1 \| None | Test set size. Float for fraction, int for count. Default: 0.1 for `train_test_split`, None for `timeseries_split` (sklearn dynamic sizing). |
"QueryCrossEntropy",
)
-# CatBoost GPU param ranges keyed by VRAM capacity (GB).
+# CatBoost GPU param ranges keyed by available VRAM (GB), not total.
# Formula: VRAM_MB = 58778 * 2^(depth-12) * (border_count+1) / 256
_CATBOOST_GPU_VRAM_PARAM_RANGES: Final[dict[int, dict[str, tuple[int, int]]]] = {
8: {"depth": (4, 9), "border_count": (32, 192), "max_ctr_complexity": (1, 4)},