用法:
dask_ml.xgboost.predict(client, model, data)
使用 XGBoost 進行分布式預測
- client: dask.distributed.Client:
- model: xgboost.Booster:
- data: dask array or dataframe:
- Dask.dataframe 或 dask.array,取決於輸入數據類型
參數:
返回:
例子:
>>> client = Client('scheduler-address:8786') >>> test_data = dd.read_csv('s3://...') >>> model <xgboost.core.Booster object at ...>
>>> predictions = predict(client, model, test_data)
相關用法
- Python dask_ml.xgboost.train用法及代碼示例
- Python dask_ml.wrappers.ParallelPostFit用法及代碼示例
- Python dask_ml.feature_extraction.text.CountVectorizer用法及代碼示例
- Python dask_ml.preprocessing.MinMaxScaler用法及代碼示例
- Python dask_ml.preprocessing.Categorizer用法及代碼示例
- Python dask_ml.linear_model.LinearRegression用法及代碼示例
- Python dask_ml.wrappers.Incremental用法及代碼示例
- Python dask_ml.metrics.mean_squared_log_error用法及代碼示例
- Python dask_ml.model_selection.GridSearchCV用法及代碼示例
- Python dask_ml.preprocessing.OrdinalEncoder用法及代碼示例
- Python dask_ml.feature_extraction.text.FeatureHasher用法及代碼示例
- Python dask_ml.preprocessing.LabelEncoder用法及代碼示例
- Python dask_ml.ensemble.BlockwiseVotingClassifier用法及代碼示例
- Python dask_ml.model_selection.train_test_split用法及代碼示例
- Python dask_ml.decomposition.PCA用法及代碼示例
- Python dask_ml.feature_extraction.text.HashingVectorizer用法及代碼示例
- Python dask_ml.preprocessing.PolynomialFeatures用法及代碼示例
- Python dask_ml.linear_model.LogisticRegression用法及代碼示例
- Python dask_ml.linear_model.PoissonRegression用法及代碼示例
- Python dask_ml.preprocessing.StandardScaler用法及代碼示例
注:本文由純淨天空篩選整理自dask.org大神的英文原創作品 dask_ml.xgboost.predict。非經特殊聲明,原始代碼版權歸原作者所有,本譯文未經允許或授權,請勿轉載或複製。