Packages

  • package root
    Definition Classes
    root
  • package ai
    Definition Classes
    root
  • package catboost
    Definition Classes
    ai
  • package spark

    CatBoost is a machine learning algorithm that uses gradient boosting on decision trees.

    CatBoost is a machine learning algorithm that uses gradient boosting on decision trees.

    Overview

    This package provides classes that implement interfaces from Apache Spark Machine Learning Library (MLLib).

    For binary and multi- classification problems use CatBoostClassifier, for regression use CatBoostRegressor.

    These classes implement usual fit method of org.apache.spark.ml.Predictor that accept a single org.apache.spark.sql.DataFrame for training, but you can also use other fit method that accepts additional datasets for computing evaluation metrics and overfitting detection similarily to CatBoost's other APIs.

    This package also contains Pool class that is CatBoost's abstraction of a dataset. It contains additional information compared to simple org.apache.spark.sql.DataFrame.

    It is also possible to create Pool with quantized features before training by calling quantize method. This is useful if this dataset is used for training multiple times and quantization parameters do not change. Pre-quantized Pool allows to cache quantized features data and so do not re-run feature quantization step at the start of an each training.

    Detailed documentation is available on https://catboost.ai/docs/

    Definition Classes
    catboost
  • package params
    Definition Classes
    spark
  • CatBoostClassificationModel
  • CatBoostClassifier
  • CatBoostPredictorTrait
  • CatBoostRegressionModel
  • CatBoostRegressor
  • Pool

object Pool extends Serializable

Companion object for Pool class that is CatBoost's abstraction of a dataset

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  12. def load(spark: SparkSession, dataPathWithScheme: String, columnDescription: Path = null, params: PoolLoadParams = new PoolLoadParams()): Pool

    Load dataset in one of CatBoost's natively supported formats:

    Load dataset in one of CatBoost's natively supported formats:

    spark

    SparkSession

    dataPathWithScheme

    Path with scheme to dataset in CatBoost format. For example, dsv:///home/user/datasets/my_dataset/train.dsv or libsvm:///home/user/datasets/my_dataset/train.libsvm

    columnDescription

    Path to column description file

    params

    Additional params specifying data format.

    returns

    Pool containing loaded data.

    Example: 

    1. val spark = SparkSession.builder()
  .master("local[*]")
  .appName("testLoadDSVSimple")
  .getOrCreate()

val pool = Pool.load(
  spark,
  "dsv:///home/user/datasets/my_dataset/train.dsv",
  columnDescription = "/home/user/datasets/my_dataset/cd"
)
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