Public

Constructor that sets base classifier for bagging to RandomTre and default
number of iterations to 1

<init>

Computes the average impurity decrease per attribute over the trees

computeAverageImpurityDecreasePerAttribute

defaultClassifierString

The default number of iterations to perform.

defaultNumberOfIterations

getBagSizePercent

getCalcOutOfBag

getClassifier

Get whether to compute and output attribute importance scores

getComputeAttributeImportance

getDoNotCheckCapabilities

getNumDecimalPlaces

getNumExecutionSlots

getNumIterations

getOutputOutOfBagComplexityStatistics

getPrintClassifiers

getStoreOutOfBagPredictions

Returns an instance of a TechnicalInformation object, containing detailed
information about the tech

getTechnicalInformation

runClassifier

setBagSizePercent

Set the preferred batch size for batch prediction.

setBatchSize

setCalcOutOfBag

LocalDateTime

Reactive rest calls using spring rest template

java.awt.Menu

A specialized Writer that writes to a file in the file system. All write
requests made by calling me

java.io.FileWriter

java.lang.String

This class represents a server-side socket that waits for incoming client
connections. A ServerSocke

java.net.ServerSocket

A specific moment in time, with millisecond precision. Values typically come
from System#currentTime

java.util.Date

HashMap is an implementation of Map. All optional operations are supported.All
elements are permitte

java.util.HashMap

getSupportFragmentManager

Return the contained value, if present, otherwise throw an exception to be
created by the provided s

orElseThrow

setContentView

/**
   * Parses a given list of options.
   * <p/>
   * 
   *  Valid options are:
   * <p>
   * 
   * <pre>
   * -P
   *  Size of each bag, as a percentage of the
   *  training set size. (default 100)
   * </pre>
   * 
   * <pre>
   * -O
   *  Calculate the out of bag error.
   * </pre>
   * 
   * <pre>
   * -store-out-of-bag-predictions
   *  Whether to store out of bag predictions in internal evaluation object.
   * </pre>
   * 
   * <pre>
   * -output-out-of-bag-complexity-statistics
   *  Whether to output complexity-based statistics when out-of-bag evaluation is performed.
   * </pre>
   * 
   * <pre>
   * -print
   *  Print the individual classifiers in the output
   * </pre>
   * 
   * <pre>
   * -attribute-importance
   *  Compute and output attribute importance (mean impurity decrease method)
   * </pre>
   * 
   * <pre>
   * -I &lt;num&gt;
   *  Number of iterations.
   *  (current value 100)
   * </pre>
   * 
   * <pre>
   * -num-slots &lt;num&gt;
   *  Number of execution slots.
   *  (default 1 - i.e. no parallelism)
   *  (use 0 to auto-detect number of cores)
   * </pre>
   * 
   * <pre>
   * -K &lt;number of attributes&gt;
   *  Number of attributes to randomly investigate. (default 0)
   *  (&lt;1 = int(log_2(#predictors)+1)).
   * </pre>
   * 
   * <pre>
   * -M &lt;minimum number of instances&gt;
   *  Set minimum number of instances per leaf.
   *  (default 1)
   * </pre>
   * 
   * <pre>
   * -V &lt;minimum variance for split&gt;
   *  Set minimum numeric class variance proportion
   *  of train variance for split (default 1e-3).
   * </pre>
   * 
   * <pre>
   * -S &lt;num&gt;
   *  Seed for random number generator.
   *  (default 1)
   * </pre>
   * 
   * <pre>
   * -depth &lt;num&gt;
   *  The maximum depth of the tree, 0 for unlimited.
   *  (default 0)
   * </pre>
   * 
   * <pre>
   * -N &lt;num&gt;
   *  Number of folds for backfitting (default 0, no backfitting).
   * </pre>
   * 
   * <pre>
   * -U
   *  Allow unclassified instances.
   * </pre>
   * 
   * <pre>
   * -B
   *  Break ties randomly when several attributes look equally good.
   * </pre>
   * 
   * <pre>
   * -output-debug-info
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console
   * </pre>
   * 
   * <pre>
   * -do-not-check-capabilities
   *  If set, classifier capabilities are not checked before classifier is built
   *  (use with caution).
   * </pre>
   * 
   * <pre>
   * -num-decimal-places
   *  The number of decimal places for the output of numbers in the model (default 2).
   * </pre>
   * 
   * <pre>
   * -batch-size
   *  The desired batch size for batch prediction  (default 100).
   * </pre>
   * 
   * 
   * 
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */

"output-out-of-bag-complexity-statistics"

How to use setClassifiermethodin weka.classifiers.trees.RandomForest

Best Java code snippets using weka.classifiers.trees.RandomForest.setClassifier (Showing top 2 results out of 315)

How to use
setClassifier
method
in
weka.classifiers.trees.RandomForest