Hyperliquid End-to-End

Build a model on CrowdCent's training data and submit¶

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!pip install crowdcent-challenge
import crowdcent_challenge as cc
import polars as pl
from xgboost import XGBRegressor
!pip install crowdcent-challenge
import crowdcent_challenge as cc
import polars as pl
from xgboost import XGBRegressor

/bin/bash: pip: command not found

For this tutorial, you will need:

CrowdCent account: register for free
CrowdCent API Key: generate an API key from your user profile

Feeling more advanced? Check out the multi-slot, multi-dataset end-to-end example

Load API key¶

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CROWDCENT_API_KEY = "API_KEY_HERE"
CROWDCENT_API_KEY = "API_KEY_HERE"

Initialize the client¶

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client = cc.ChallengeClient(
    challenge_slug="hyperliquid-ranking",
    api_key=CROWDCENT_API_KEY,
)
client = cc.ChallengeClient(
    challenge_slug="hyperliquid-ranking",
    api_key=CROWDCENT_API_KEY,
)

2026-02-04 21:19:40,206 - INFO - ChallengeClient initialized for 'hyperliquid-ranking' at URL: https://crowdcent.com/api

Get CrowdCent's training data¶

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client.download_training_dataset(version="latest", dest_path="training_data.parquet")

training_data = pl.read_parquet("training_data.parquet")
training_data.head()
client.download_training_dataset(version="latest", dest_path="training_data.parquet")

training_data = pl.read_parquet("training_data.parquet")
training_data.head()

2026-02-04 21:19:40,508 - INFO - Downloading training data v2.0 to training_data.parquet
Downloading training_data.parquet: 100%|██████████| 124M/124M [00:02<00:00, 55.0MB/s] 
2026-02-04 21:19:43,278 - INFO - Successfully downloaded training data v2.0 to training_data.parquet

Out[4]:

shape: (5, 85)

id	eodhd_id	date	feature_16_lag15	feature_13_lag15	feature_14_lag15	feature_15_lag15	feature_8_lag15	feature_5_lag15	feature_6_lag15	feature_7_lag15	feature_12_lag15	feature_9_lag15	feature_10_lag15	feature_11_lag15	feature_4_lag15	feature_1_lag15	feature_2_lag15	feature_3_lag15	feature_20_lag15	feature_17_lag15	feature_18_lag15	feature_19_lag15	feature_16_lag10	feature_13_lag10	feature_14_lag10	feature_15_lag10	feature_8_lag10	feature_5_lag10	feature_6_lag10	feature_7_lag10	feature_12_lag10	feature_9_lag10	feature_10_lag10	feature_11_lag10	feature_4_lag10	feature_1_lag10	…	feature_5_lag5	feature_6_lag5	feature_7_lag5	feature_12_lag5	feature_9_lag5	feature_10_lag5	feature_11_lag5	feature_4_lag5	feature_1_lag5	feature_2_lag5	feature_3_lag5	feature_20_lag5	feature_17_lag5	feature_18_lag5	feature_19_lag5	feature_16_lag0	feature_13_lag0	feature_14_lag0	feature_15_lag0	feature_8_lag0	feature_5_lag0	feature_6_lag0	feature_7_lag0	feature_12_lag0	feature_9_lag0	feature_10_lag0	feature_11_lag0	feature_4_lag0	feature_1_lag0	feature_2_lag0	feature_3_lag0	feature_20_lag0	feature_17_lag0	feature_18_lag0	feature_19_lag0	target_10d	target_30d
str	str	datetime[μs]	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	…	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64
"0G"	"0G-USD.CC"	2025-11-16 00:00:00	0.157692	0.156336	0.239762	0.313349	0.051923	0.135122	0.269735	0.300353	0.189423	0.200007	0.227781	0.336593	0.161538	0.145204	0.238061	0.291121	0.605769	0.587816	0.566855	0.516127	0.264423	0.211058	0.241617	0.296768	0.329808	0.190865	0.256535	0.31913	0.407692	0.298558	0.277047	0.321981	0.257692	0.209615	…	0.353349	0.244235	0.33943	0.549011	0.478352	0.339179	0.344593	0.496117	0.376905	0.261054	0.311914	0.270146	0.324496	0.456156	0.475274	0.522488	0.5093	0.360179	0.328206	0.611483	0.494187	0.342526	0.351089	0.536842	0.542926	0.420742	0.331381	0.524402	0.510259	0.359937	0.322647	0.588517	0.429332	0.46082	0.494638	0.832536	0.186603
"0G"	"0G-USD.CC"	2025-11-17 00:00:00	0.160577	0.145192	0.238297	0.291124	0.035577	0.134135	0.257164	0.280925	0.021154	0.200481	0.224395	0.312074	0.033654	0.146154	0.224768	0.267206	0.609615	0.603365	0.603132	0.512047	0.257692	0.209135	0.236536	0.277681	0.332692	0.184135	0.25169	0.317794	0.417308	0.219231	0.271717	0.302891	0.334615	0.184135	…	0.420793	0.277464	0.332612	0.639856	0.528582	0.364531	0.333954	0.594281	0.464448	0.305301	0.309641	0.431731	0.338462	0.470913	0.474799	0.524402	0.51072	0.359927	0.322639	0.470813	0.489853	0.336994	0.348187	0.466986	0.553421	0.386326	0.333876	0.41244	0.503361	0.343748	0.328964	0.400957	0.416344	0.421874	0.471446	0.779904	0.167464
"0G"	"0G-USD.CC"	2025-11-18 00:00:00	0.032692	0.145673	0.225003	0.267208	0.225	0.228846	0.280594	0.305561	0.215385	0.281731	0.264013	0.313656	0.225962	0.228365	0.268489	0.291843	0.769231	0.639423	0.616418	0.536684	0.334615	0.183654	0.25431	0.283911	0.332692	0.278846	0.297583	0.341345	0.392308	0.303846	0.313781	0.324164	0.335577	0.280769	…	0.339453	0.28415	0.320811	0.62851	0.510409	0.39607	0.330011	0.501458	0.418518	0.323442	0.322918	0.242262	0.243727	0.441575	0.474793	0.41244	0.503821	0.343737	0.329076	0.484211	0.415212	0.347029	0.348368	0.355024	0.491767	0.397807	0.338166	0.453589	0.477523	0.379146	0.343882	0.476555	0.359408	0.43331	0.48185	0.885167	0.22488
"0G"	"0G-USD.CC"	2025-11-19 00:00:00	0.225	0.227885	0.268724	0.291845	0.327885	0.270673	0.302442	0.319021	0.409615	0.377404	0.30822	0.338053	0.261538	0.217788	0.270617	0.296166	0.607692	0.584615	0.588395	0.535952	0.335577	0.280288	0.29972	0.30867	0.372115	0.35	0.290399	0.359733	0.394231	0.401923	0.355811	0.347837	0.495192	0.378365	…	0.316703	0.293688	0.328585	0.489759	0.441995	0.409699	0.335865	0.335922	0.415557	0.316673	0.324101	0.228722	0.250419	0.417517	0.471778	0.453589	0.477984	0.379136	0.343994	0.567464	0.414377	0.382189	0.347465	0.36555	0.427655	0.414789	0.32552	0.440191	0.388057	0.383211	0.325019	0.552153	0.390438	0.415171	0.485846	0.870813	0.301435
"0G"	"0G-USD.CC"	2025-11-20 00:00:00	0.260577	0.217308	0.270853	0.296168	0.329808	0.251923	0.289152	0.318531	0.414423	0.300481	0.283331	0.338529	0.265385	0.211538	0.241381	0.296764	0.445192	0.578846	0.564472	0.513369	0.495192	0.377885	0.301414	0.332464	0.377885	0.353846	0.243044	0.338834	0.546154	0.480288	0.346681	0.3446	0.495192	0.380288	…	0.402339	0.327131	0.328132	0.361722	0.453938	0.377209	0.31916	0.522488	0.50884	0.360189	0.328094	0.419139	0.342742	0.460794	0.47396	0.440191	0.388517	0.383201	0.325131	0.402871	0.414833	0.384339	0.327887	0.42488	0.393301	0.436795	0.332415	0.308134	0.415311	0.3978	0.318543	0.427751	0.423445	0.389607	0.490231	0.799043	0.124402

Train a model on the training data¶

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xgb_regressor = XGBRegressor(n_estimators=200, device="cuda")
feature_names = [col for col in training_data.columns if col.startswith("feature")]

xgb_regressor.fit(
    training_data[feature_names],
    training_data[["target_10d", "target_30d"]],
)
xgb_regressor = XGBRegressor(n_estimators=200, device="cuda")
feature_names = [col for col in training_data.columns if col.startswith("feature")]

xgb_regressor.fit(
    training_data[feature_names],
    training_data[["target_10d", "target_30d"]],
)

Out[5]:

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device='cuda', early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             feature_weights=None, gamma=None, grow_policy=None,
             importance_type=None, interaction_constraints=None,
             learning_rate=None, max_bin=None, max_cat_threshold=None,
             max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
             max_leaves=None, min_child_weight=None, missing=nan,
             monotone_constraints=None, multi_strategy=None, n_estimators=200,
             n_jobs=None, num_parallel_tree=None, ...)

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Get CrowdCent's latest inference data¶

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client.download_inference_data("latest", "inference_data.parquet")

inference_data = pl.read_parquet("inference_data.parquet")
inference_data.head()
client.download_inference_data("latest", "inference_data.parquet")

inference_data = pl.read_parquet("inference_data.parquet")
inference_data.head()

2026-02-04 21:19:46,297 - INFO - Downloading inference data 2026-02-04 to inference_data.parquet
Downloading inference_data.parquet: 100%|██████████| 138k/138k [00:00<00:00, 65.9MB/s]
2026-02-04 21:19:46,465 - INFO - Successfully downloaded inference data 2026-02-04 to inference_data.parquet

Out[6]:

shape: (5, 83)

id	eodhd_id	date	feature_16_lag15	feature_13_lag15	feature_14_lag15	feature_15_lag15	feature_8_lag15	feature_5_lag15	feature_6_lag15	feature_7_lag15	feature_12_lag15	feature_9_lag15	feature_10_lag15	feature_11_lag15	feature_4_lag15	feature_1_lag15	feature_2_lag15	feature_3_lag15	feature_20_lag15	feature_17_lag15	feature_18_lag15	feature_19_lag15	feature_16_lag10	feature_13_lag10	feature_14_lag10	feature_15_lag10	feature_8_lag10	feature_5_lag10	feature_6_lag10	feature_7_lag10	feature_12_lag10	feature_9_lag10	feature_10_lag10	feature_11_lag10	feature_4_lag10	feature_1_lag10	…	feature_15_lag5	feature_8_lag5	feature_5_lag5	feature_6_lag5	feature_7_lag5	feature_12_lag5	feature_9_lag5	feature_10_lag5	feature_11_lag5	feature_4_lag5	feature_1_lag5	feature_2_lag5	feature_3_lag5	feature_20_lag5	feature_17_lag5	feature_18_lag5	feature_19_lag5	feature_16_lag0	feature_13_lag0	feature_14_lag0	feature_15_lag0	feature_8_lag0	feature_5_lag0	feature_6_lag0	feature_7_lag0	feature_12_lag0	feature_9_lag0	feature_10_lag0	feature_11_lag0	feature_4_lag0	feature_1_lag0	feature_2_lag0	feature_3_lag0	feature_20_lag0	feature_17_lag0	feature_18_lag0	feature_19_lag0
str	str	datetime[μs]	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	…	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64
"BIO"	"BIO.CC"	2026-02-03 00:00:00	0.598925	0.46837	0.450671	0.487363	0.515054	0.55032	0.502187	0.518391	0.67957	0.547472	0.506168	0.515922	0.652688	0.546998	0.487283	0.487425	0.530108	0.512671	0.498768	0.4706	0.703226	0.651075	0.516748	0.510942	0.705376	0.610215	0.533936	0.543455	0.736559	0.708065	0.559768	0.540695	0.686022	0.669355	…	0.496118	0.247312	0.476344	0.513332	0.518288	0.163441	0.45	0.498736	0.500854	0.2	0.443011	0.495004	0.485205	0.389247	0.476882	0.494776	0.491848	0.28165	0.31018	0.480627	0.464973	0.240447	0.243879	0.427047	0.465506	0.216531	0.189986	0.449025	0.458326	0.301702	0.250851	0.460103	0.453864	0.501737	0.445492	0.496402	0.476321
"JUP"	"JUP29210-USD.CC"	2026-02-03 00:00:00	0.519355	0.51587	0.551449	0.538697	0.431183	0.51299	0.554874	0.524464	0.507527	0.512993	0.543253	0.519194	0.441935	0.515705	0.537852	0.536899	0.484946	0.51145	0.517347	0.506106	0.539785	0.52957	0.533422	0.560765	0.55914	0.495161	0.537902	0.561383	0.534409	0.520968	0.526315	0.548022	0.594624	0.51828	…	0.561927	0.597849	0.578495	0.545743	0.558006	0.733333	0.633871	0.573432	0.554959	0.622581	0.608602	0.562153	0.555401	0.629032	0.553763	0.532607	0.515087	0.50166	0.591153	0.560361	0.545175	0.470844	0.534347	0.514754	0.532943	0.53588	0.634607	0.577787	0.543836	0.487493	0.555037	0.536658	0.538635	0.497737	0.563385	0.522553	0.509872
"BABY"	"BABY32198-USD.CC"	2026-02-03 00:00:00	0.648387	0.573011	0.498397	0.509604	0.689247	0.484522	0.469018	0.503833	0.688172	0.5124	0.512416	0.515668	0.683871	0.551645	0.499336	0.505479	0.573118	0.468003	0.473191	0.472271	0.636559	0.642473	0.564834	0.528498	0.651613	0.67043	0.545164	0.521096	0.6	0.644086	0.570795	0.521884	0.578495	0.631183	…	0.520413	0.677419	0.664516	0.574519	0.525908	0.673118	0.636559	0.57448	0.541699	0.703226	0.64086	0.596253	0.531505	0.54086	0.513441	0.490722	0.48428	0.68777	0.626681	0.634577	0.546385	0.626864	0.652142	0.661286	0.541834	0.722811	0.697965	0.671025	0.574348	0.613299	0.658262	0.644723	0.550194	0.313801	0.427331	0.47845	0.451884
"CHILLGUY"	"CHILLGUY-USD.CC"	2026-02-03 00:00:00	0.229032	0.407475	0.562386	0.515923	0.239785	0.367155	0.546821	0.52314	0.268817	0.390139	0.565881	0.521184	0.250538	0.332299	0.537501	0.515237	0.53871	0.49311	0.495474	0.523818	0.224731	0.226882	0.404785	0.488474	0.331183	0.285484	0.411779	0.495078	0.143011	0.205914	0.411363	0.466087	0.347312	0.298925	…	0.492309	0.522581	0.426882	0.397018	0.499996	0.533333	0.338172	0.364156	0.480456	0.402151	0.374731	0.353515	0.47973	0.434409	0.477957	0.485533	0.51101	0.423053	0.473355	0.350118	0.481812	0.501182	0.511881	0.398683	0.497914	0.500508	0.516921	0.361417	0.490587	0.547052	0.474601	0.386763	0.487977	0.561231	0.49782	0.513964	0.512921
"SPX"	"SPX28081-USD.CC"	2026-02-03 00:00:00	0.323656	0.480026	0.50731	0.484061	0.21828	0.418454	0.470578	0.443127	0.258065	0.437998	0.472512	0.456526	0.263441	0.418997	0.473823	0.476101	0.5	0.493638	0.514927	0.492058	0.184946	0.254301	0.377331	0.45326	0.109677	0.163978	0.325295	0.43062	0.222581	0.240323	0.368158	0.45489	0.156989	0.210215	…	0.447719	0.42043	0.265054	0.341754	0.435607	0.473118	0.347849	0.392924	0.453489	0.41828	0.287634	0.353316	0.441658	0.413978	0.504301	0.49897	0.508269	0.375854	0.388464	0.321383	0.436187	0.342887	0.381658	0.272818	0.411711	0.5176	0.495359	0.367841	0.456702	0.420826	0.419553	0.314884	0.43291	0.539673	0.476826	0.512069	0.511539

Make predictions on the inference data¶

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preds = xgb_regressor.predict(inference_data[feature_names])
pred_df = pl.from_numpy(preds, ["pred_10d", "pred_30d"])
preds = xgb_regressor.predict(inference_data[feature_names])
pred_df = pl.from_numpy(preds, ["pred_10d", "pred_30d"])

/home/exx/projects/crowdcent-challenge/.venv/lib/python3.12/site-packages/xgboost/core.py:774: UserWarning: [21:19:46] WARNING: /workspace/src/common/error_msg.cc:62: Falling back to prediction using DMatrix due to mismatched devices. This might lead to higher memory usage and slower performance. XGBoost is running on: cuda:0, while the input data is on: cpu.
Potential solutions:
- Use a data structure that matches the device ordinal in the booster.
- Set the device for booster before call to inplace_predict.

This warning will only be shown once.

  return func(**kwargs)

Submit to the `hyperliquid-ranking` challenge on CrowdCent¶

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pred_df = pred_df.with_columns(inference_data["id"]).select(
    ["id", "pred_10d", "pred_30d"]
)

# ensure predictions are between 0 and 1
pred_df = pred_df.with_columns(pl.col(["pred_10d", "pred_30d"]).clip(0, 1))

with pl.Config(tbl_rows=20):
    display(pred_df.sort("pred_30d", descending=True))
pred_df = pred_df.with_columns(inference_data["id"]).select(
    ["id", "pred_10d", "pred_30d"]
)

# ensure predictions are between 0 and 1
pred_df = pred_df.with_columns(pl.col(["pred_10d", "pred_30d"]).clip(0, 1))

with pl.Config(tbl_rows=20):
    display(pred_df.sort("pred_30d", descending=True))

shape: (186, 3)

id	pred_10d	pred_30d
str	f32	f32
"SOPH"	0.481924	0.825876
"BERA"	0.363361	0.745414
"HYPE"	0.44265	0.693696
"XPL"	0.705975	0.667599
"MERL"	0.624524	0.642958
"STRK"	0.599383	0.632716
"ALGO"	0.541823	0.620179
"TRX"	0.494462	0.608403
"LDO"	0.540888	0.608148
"ZRO"	0.447225	0.607567
…	…	…
"JTO"	0.294615	0.351979
"TNSR"	0.410136	0.350033
"AVNT"	0.198144	0.342407
"XMR"	0.495307	0.339069
"BIO"	0.316203	0.298175
"LAYER"	0.630956	0.290479
"DASH"	0.309354	0.289107
"BLUR"	0.241445	0.286826
"CC"	0.676866	0.24413
"AXS"	0.536369	0.209781

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# directly submit a dataframe to slot 1
client.submit_predictions(df=pred_df, slot=1)
# directly submit a dataframe to slot 1
client.submit_predictions(df=pred_df, slot=1)

2026-02-04 21:19:46,617 - INFO - Wrote DataFrame to temporary file: submission.parquet
2026-02-04 21:19:46,618 - INFO - Submitting predictions from submission.parquet to challenge 'hyperliquid-ranking' (Slot: 1)
2026-02-04 21:19:47,091 - INFO - Submission queued (slot 1)

Out[9]:

{'match_info': {'matched_ids': 186,
  'unmatched_ids': 0,
  'message': '186 IDs matched inference data'},
 'status': 'queued',
 'message': 'Submission queued for slot 1. Will be automatically submitted when next period opens.',
 'slot': 1,
 'challenge': 'hyperliquid-ranking'}

	objective objective: typing.Union[str, xgboost.sklearn._SklObjWProto, typing.Callable[[typing.Any, typing.Any], typing.Tuple[numpy.ndarray, numpy.ndarray]], NoneType] Specify the learning task and the corresponding learning objective or a custom objective function to be used. For custom objective, see :doc:`/tutorials/custom_metric_obj` and :ref:`custom-obj-metric` for more information, along with the end note for function signatures.	'reg:squarederror'
	base_score base_score: typing.Union[float, typing.List[float], NoneType] The initial prediction score of all instances, global bias.	None
	booster	None
	callbacks callbacks: typing.Optional[typing.List[xgboost.callback.TrainingCallback]] List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using :ref:`Callback API `. .. note:: States in callback are not preserved during training, which means callback objects can not be reused for multiple training sessions without reinitialization or deepcopy. .. code-block:: python for params in parameters_grid: # be sure to (re)initialize the callbacks before each run callbacks = [xgb.callback.LearningRateScheduler(custom_rates)] reg = xgboost.XGBRegressor(**params, callbacks=callbacks) reg.fit(X, y)	None
	colsample_bylevel colsample_bylevel: typing.Optional[float] Subsample ratio of columns for each level.	None
	colsample_bynode colsample_bynode: typing.Optional[float] Subsample ratio of columns for each split.	None
	colsample_bytree colsample_bytree: typing.Optional[float] Subsample ratio of columns when constructing each tree.	None
	device device: typing.Optional[str] .. versionadded:: 2.0.0 Device ordinal, available options are `cpu`, `cuda`, and `gpu`.	'cuda'
	early_stopping_rounds early_stopping_rounds: typing.Optional[int] .. versionadded:: 1.6.0 - Activates early stopping. Validation metric needs to improve at least once in every early_stopping_rounds round(s) to continue training. Requires at least one item in eval_set in :py:meth:`fit`. - If early stopping occurs, the model will have two additional attributes: :py:attr:`best_score` and :py:attr:`best_iteration`. These are used by the :py:meth:`predict` and :py:meth:`apply` methods to determine the optimal number of trees during inference. If users want to access the full model (including trees built after early stopping), they can specify the `iteration_range` in these inference methods. In addition, other utilities like model plotting can also use the entire model. - If you prefer to discard the trees after `best_iteration`, consider using the callback function :py:class:`xgboost.callback.EarlyStopping`. - If there's more than one item in eval_set, the last entry will be used for early stopping. If there's more than one metric in eval_metric, the last metric will be used for early stopping.	None
	enable_categorical enable_categorical: bool See the same parameter of :py:class:`DMatrix` for details.	False
	eval_metric eval_metric: typing.Union[str, typing.List[typing.Union[str, typing.Callable]], typing.Callable, NoneType] .. versionadded:: 1.6.0 Metric used for monitoring the training result and early stopping. It can be a string or list of strings as names of predefined metric in XGBoost (See :doc:`/parameter`), one of the metrics in :py:mod:`sklearn.metrics`, or any other user defined metric that looks like `sklearn.metrics`. If custom objective is also provided, then custom metric should implement the corresponding reverse link function. Unlike the `scoring` parameter commonly used in scikit-learn, when a callable object is provided, it's assumed to be a cost function and by default XGBoost will minimize the result during early stopping. For advanced usage on Early stopping like directly choosing to maximize instead of minimize, see :py:obj:`xgboost.callback.EarlyStopping`. See :doc:`/tutorials/custom_metric_obj` and :ref:`custom-obj-metric` for more information. .. code-block:: python from sklearn.datasets import load_diabetes from sklearn.metrics import mean_absolute_error X, y = load_diabetes(return_X_y=True) reg = xgb.XGBRegressor( tree_method="hist", eval_metric=mean_absolute_error, ) reg.fit(X, y, eval_set=[(X, y)])	None
	feature_types feature_types: typing.Optional[typing.Sequence[str]] .. versionadded:: 1.7.0 Used for specifying feature types without constructing a dataframe. See the :py:class:`DMatrix` for details.	None
	feature_weights feature_weights: Optional[ArrayLike] Weight for each feature, defines the probability of each feature being selected when colsample is being used. All values must be greater than 0, otherwise a `ValueError` is thrown.	None
	gamma gamma: typing.Optional[float] (min_split_loss) Minimum loss reduction required to make a further partition on a leaf node of the tree.	None
	grow_policy grow_policy: typing.Optional[str] Tree growing policy. - depthwise: Favors splitting at nodes closest to the node, - lossguide: Favors splitting at nodes with highest loss change.	None
	importance_type	None
	interaction_constraints interaction_constraints: typing.Union[str, typing.List[typing.Tuple[str]], NoneType] Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nested list, e.g. ``[[0, 1], [2, 3, 4]]``, where each inner list is a group of indices of features that are allowed to interact with each other. See :doc:`tutorial ` for more information	None
	learning_rate learning_rate: typing.Optional[float] Boosting learning rate (xgb's "eta")	None
	max_bin max_bin: typing.Optional[int] If using histogram-based algorithm, maximum number of bins per feature	None
	max_cat_threshold max_cat_threshold: typing.Optional[int] .. versionadded:: 1.7.0 .. note:: This parameter is experimental Maximum number of categories considered for each split. Used only by partition-based splits for preventing over-fitting. Also, `enable_categorical` needs to be set to have categorical feature support. See :doc:`Categorical Data ` and :ref:`cat-param` for details.	None
	max_cat_to_onehot max_cat_to_onehot: Optional[int] .. versionadded:: 1.6.0 .. note:: This parameter is experimental A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes. Also, `enable_categorical` needs to be set to have categorical feature support. See :doc:`Categorical Data ` and :ref:`cat-param` for details.	None
	max_delta_step max_delta_step: typing.Optional[float] Maximum delta step we allow each tree's weight estimation to be.	None
	max_depth max_depth: typing.Optional[int] Maximum tree depth for base learners.	None
	max_leaves max_leaves: typing.Optional[int] Maximum number of leaves; 0 indicates no limit.	None
	min_child_weight min_child_weight: typing.Optional[float] Minimum sum of instance weight(hessian) needed in a child.	None
	missing missing: float Value in the data which needs to be present as a missing value. Default to :py:data:`numpy.nan`.	nan
	monotone_constraints monotone_constraints: typing.Union[typing.Dict[str, int], str, NoneType] Constraint of variable monotonicity. See :doc:`tutorial ` for more information.	None
	multi_strategy multi_strategy: typing.Optional[str] .. versionadded:: 2.0.0 .. note:: This parameter is working-in-progress. The strategy used for training multi-target models, including multi-target regression and multi-class classification. See :doc:`/tutorials/multioutput` for more information. - ``one_output_per_tree``: One model for each target. - ``multi_output_tree``: Use multi-target trees.	None
	n_estimators n_estimators: typing.Optional[int] Number of gradient boosted trees. Equivalent to number of boosting rounds.	200
	n_jobs n_jobs: typing.Optional[int] Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.	None
	num_parallel_tree	None
	random_state random_state: typing.Union[numpy.random.mtrand.RandomState, numpy.random._generator.Generator, int, NoneType] Random number seed. .. note:: Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.	None
	reg_alpha reg_alpha: typing.Optional[float] L1 regularization term on weights (xgb's alpha).	None
	reg_lambda reg_lambda: typing.Optional[float] L2 regularization term on weights (xgb's lambda).	None
	sampling_method sampling_method: typing.Optional[str] Sampling method. Used only by the GPU version of ``hist`` tree method. - ``uniform``: Select random training instances uniformly. - ``gradient_based``: Select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost)	None
	scale_pos_weight scale_pos_weight: typing.Optional[float] Balancing of positive and negative weights.	None
	subsample subsample: typing.Optional[float] Subsample ratio of the training instance.	None
	tree_method tree_method: typing.Optional[str] Specify which tree method to use. Default to auto. If this parameter is set to default, XGBoost will choose the most conservative option available. It's recommended to study this option from the parameters document :doc:`tree method `	None
	validate_parameters validate_parameters: typing.Optional[bool] Give warnings for unknown parameter.	None
	verbosity verbosity: typing.Optional[int] The degree of verbosity. Valid values are 0 (silent) - 3 (debug).	None