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

Creating JSON documents from java classes using gson

This Graphics2D class extends the Graphics class to provide more sophisticated
control overgraphics 

java.awt.Graphics2D

Thrown when a program encounters the end of a file or stream during an input
operation.

java.io.EOFException

An immutable arbitrary-precision signed integer.FAST CRYPTOGRAPHY
This implementation is efficient f

java.math.BigInteger

This class contains various methods for manipulating arrays (such as sorting and
searching). This cl

java.util.Arrays

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

java.util.HashMap

Defines a general exception a servlet can throw when it encounters difficulty.

javax.servlet.ServletException

setContentView

scheduleAtFixedRate

Return the original filename in the client's filesystem.This may contain path
information depending 

getOriginalFilename

/**
   * 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 setSeedmethodin weka.classifiers.trees.RandomForest

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

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