Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://spark.apache.org/docs/latest/api/java/org/apache/spark/ml/classification/LinearSVC.html below:

LinearSVC (Spark 4.0.0 JavaDoc)

All Implemented Interfaces:

Serializable, org.apache.spark.internal.Logging, ClassifierParams, LinearSVCParams, Params, HasAggregationDepth, HasFeaturesCol, HasFitIntercept, HasLabelCol, HasMaxBlockSizeInMB, HasMaxIter, HasPredictionCol, HasRawPredictionCol, HasRegParam, HasStandardization, HasThreshold, HasTol, HasWeightCol, PredictorParams, DefaultParamsWritable, Identifiable, MLWritable

This binary classifier optimizes the Hinge Loss using the OWLQN optimizer. Only supports L2 regularization currently.

Since 3.1.0, it supports stacking instances into blocks and using GEMV for better performance. The block size will be 1.0 MB, if param maxBlockSizeInMB is set 0.0 by default.

See Also:

• Serialized Form

• Nested Class Summary
  Nested classes/interfaces inherited from interface org.apache.spark.internal.Loggingorg.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter

• Constructor Summary

  Constructors

• Method Summary

  Param for suggested depth for treeAggregate (>= 2).

  Creates a copy of this instance with the same UID and some extra params.

  Param for whether to fit an intercept term.

  Param for Maximum memory in MB for stacking input data into blocks.

  Param for maximum number of iterations (>= 0).

  Param for regularization parameter (>= 0).

  Suggested depth for treeAggregate (greater than or equal to 2).

  Whether to fit an intercept term.

  Set the maximum number of iterations.

  Set the regularization parameter.

  Whether to standardize the training features before fitting the model.

  Set threshold in binary classification.

  Set the convergence tolerance of iterations.

  Param for whether to standardize the training features before fitting the model.

  Param for threshold in binary classification prediction.

  Param for the convergence tolerance for iterative algorithms (>= 0).

  An immutable unique ID for the object and its derivatives.

  Param for weight column name.

  Methods inherited from interface org.apache.spark.internal.LogginginitializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext
  Methods inherited from interface org.apache.spark.ml.util.MLWritablesave Methods inherited from interface org.apache.spark.ml.param.Paramsclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

• Constructor Details
  • LinearSVC
  • LinearSVC

    public LinearSVC()

• Method Details
  • load
  • read
  • threshold

    Specified by:

    threshold in interface HasThreshold

    Specified by:

    threshold in interface LinearSVCParams

    Returns:

    (undocumented)

  • maxBlockSizeInMB

    Param for Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0..

    Specified by:

    maxBlockSizeInMB in interface HasMaxBlockSizeInMB

    Returns:

    (undocumented)

  • aggregationDepth public final IntParam aggregationDepth

    ()

    Param for suggested depth for treeAggregate (>= 2).

    Specified by:

    aggregationDepth in interface HasAggregationDepth

    Returns:

    (undocumented)

  • weightCol

    Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

    Specified by:

    weightCol in interface HasWeightCol

    Returns:

    (undocumented)

  • standardization

    Param for whether to standardize the training features before fitting the model.

    Specified by:

    standardization in interface HasStandardization

    Returns:

    (undocumented)

  • tol Description copied from interface: HasTol

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Specified by:

    tol in interface HasTol

    Returns:

    (undocumented)

  • fitIntercept

    Param for whether to fit an intercept term.

    Specified by:

    fitIntercept in interface HasFitIntercept

    Returns:

    (undocumented)

  • maxIter

    Param for maximum number of iterations (>= 0).

    Specified by:

    maxIter in interface HasMaxIter

    Returns:

    (undocumented)

  • regParam

    Param for regularization parameter (>= 0).

    Specified by:

    regParam in interface HasRegParam

    Returns:

    (undocumented)

  • uid

    An immutable unique ID for the object and its derivatives.

    Specified by:

    uid in interface Identifiable

    Returns:

    (undocumented)

  • setRegParam

    Parameters:

    value - (undocumented)

    Returns:

    (undocumented)

  • setMaxIter

    Parameters:

    value - (undocumented)

    Returns:

    (undocumented)

  • setFitIntercept public LinearSVC setFitIntercept(boolean value)

    Parameters:

    value - (undocumented)

    Returns:

    (undocumented)

  • setTol

    Parameters:

    value - (undocumented)

    Returns:

    (undocumented)

  • setStandardization public LinearSVC setStandardization(boolean value)

    Parameters:

    value - (undocumented)

    Returns:

    (undocumented)

  • setWeightCol

    Set the value of param

    weightCol()

    . If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one.

    Parameters:

    value - (undocumented)

    Returns:

    (undocumented)

  • setThreshold

    Parameters:

    value - (undocumented)

    Returns:

    (undocumented)

  • setAggregationDepth public LinearSVC setAggregationDepth(int value)

    Parameters:

    value - (undocumented)

    Returns:

    (undocumented)

  • setMaxBlockSizeInMB public LinearSVC setMaxBlockSizeInMB(double value)

    Parameters:

    value - (undocumented)

    Returns:

    (undocumented)

  • copy Description copied from interface: Params

    Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

    Specified by:

    copy in interface Params

    Specified by:

    copy in class Predictor<Vector,LinearSVC,LinearSVCModel>

    Parameters:

    extra - (undocumented)

    Returns:

    (undocumented)

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4