{"library":"sparkxgb","title":"sparkxgb","type":"library","description":"sparkxgb is a Python wrapper for XGBoost on Apache Spark, providing integration utilities for distributed training and prediction on Spark DataFrames. Version 0.4 is stable but infrequently updated; rely on official XGBoost Spark integration for newer APIs.","language":"python","status":"maintenance","last_verified":"Fri May 01","install":{"commands":["pip install sparkxgb"],"cli":null},"imports":["from sparkxgb import XGBoostEstimator","from sparkxgb import XGBoostClassificationModel"],"auth":{"required":false,"env_vars":[]},"links":{"homepage":null,"github":null,"docs":null,"changelog":null,"pypi":"https://pypi.org/project/sparkxgb/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null},"quickstart":{"code":"from pyspark.sql import SparkSession\nfrom sparkxgb import XGBoostClassifier\n\nspark = SparkSession.builder.appName('sparkxgb-example').getOrCreate()\ndf = spark.createDataFrame([(1.0, [0.1, 0.2]), (0.0, [0.3, 0.4])], ['label', 'features'])\nclassifier = XGBoostClassifier(max_depth=3, num_round=10)\nmodel = classifier.fit(df, params={'eval_metric': 'logloss'})\npredictions = model.transform(df)\npredictions.show()","lang":"python","description":"Train an XGBoost classifier on a Spark DataFrame with feature vector column.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}