Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark
    Definition Classes
    apache
  • package mllib

    RDD-based machine learning APIs (in maintenance mode).

    RDD-based machine learning APIs (in maintenance mode).

    The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,

    • no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
    • bug fixes in the RDD-based APIs will still be accepted.

    The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.

    Definition Classes
    spark
    See also

    SPARK-4591 to track the progress of feature parity

  • package tree

    This package contains the default implementation of the decision tree algorithm, which supports:

    This package contains the default implementation of the decision tree algorithm, which supports:

    • binary classification,
    • regression,
    • information loss calculation with entropy and Gini for classification and variance for regression,
    • both continuous and categorical features.
    Definition Classes
    mllib
  • package configuration
    Definition Classes
    tree
  • package impurity
    Definition Classes
    tree
  • package loss
    Definition Classes
    tree
  • AbsoluteError
  • LogLoss
  • Loss
  • Losses
  • SquaredError
  • package model
    Definition Classes
    tree

package loss

Ordering
  1. Alphabetic
Visibility
  1. Public
  2. Protected

Type Members

  1. trait Loss extends Serializable

    Trait for adding "pluggable" loss functions for the gradient boosting algorithm.

    Trait for adding "pluggable" loss functions for the gradient boosting algorithm.

    Annotations
    @Since("1.2.0")

Value Members

  1. object AbsoluteError extends Loss

    Class for absolute error loss calculation (for regression).

    Class for absolute error loss calculation (for regression).

    The absolute (L1) error is defined as: |y - F(x)| where y is the label and F(x) is the model prediction for features x.

    Annotations
    @Since("1.2.0")
  2. object LogLoss extends ClassificationLoss

    Class for log loss calculation (for classification).

    Class for log loss calculation (for classification). This uses twice the binomial negative log likelihood, called "deviance" in Friedman (1999).

    The log loss is defined as: 2 log(1 + exp(-2 y F(x))) where y is a label in {-1, 1} and F(x) is the model prediction for features x.

    Annotations
    @Since("1.2.0")
  3. object Losses
    Annotations
    @Since("1.2.0")
  4. object SquaredError extends Loss

    Class for squared error loss calculation.

    Class for squared error loss calculation.

    The squared (L2) error is defined as: (y - F(x))**2 where y is the label and F(x) is the model prediction for features x.

    Annotations
    @Since("1.2.0")

Ungrouped