How to use

getClassifier

method

in

weka.classifiers.trees.RandomForest

/**
 * Returns the tip text for this property
 *
 * @return tip text for this property suitable for displaying in the
 *         explorer/experimenter gui
 */
public String breakTiesRandomlyTipText() {
 return ((RandomTree) getClassifier()).breakTiesRandomlyTipText();
}

/**
 * Get the number of features used in random selection.
 *
 * @return Value of numFeatures.
 */
public int getNumFeatures() {
 return ((RandomTree) getClassifier()).getKValue();
}

/**
 * Set the maximum depth of the tree, 0 for unlimited.
 *
 * @param value the maximum depth.
 */
public void setMaxDepth(int value) {
 ((RandomTree) getClassifier()).setMaxDepth(value);
}

/**
 * Returns the tip text for this property
 *
 * @return tip text for this property suitable for displaying in the
 *         explorer/experimenter gui
 */
public String breakTiesRandomlyTipText() {
 return ((RandomTree) getClassifier()).breakTiesRandomlyTipText();
}

/**
 * Returns the tip text for this property
 *
 * @return tip text for this property suitable for displaying in the
 *         explorer/experimenter gui
 */
public String numFeaturesTipText() {
 return ((RandomTree) getClassifier()).KValueTipText();
}

/**
 * Set the number of features to use in random selection.
 *
 * @param newNumFeatures Value to assign to numFeatures.
 */
public void setNumFeatures(int newNumFeatures) {
 ((RandomTree) getClassifier()).setKValue(newNumFeatures);
}

/**
 * Get the maximum depth of trh tree, 0 for unlimited.
 *
 * @return the maximum depth.
 */
public int getMaxDepth() {
 return ((RandomTree) getClassifier()).getMaxDepth();
}

/**
 * Get the number of features used in random selection.
 *
 * @return Value of numFeatures.
 */
public int getNumFeatures() {
 return ((RandomTree) getClassifier()).getKValue();
}

/**
 * Get whether to break ties randomly.
 *
 * @return true if ties are to be broken randomly.
 */
public boolean getBreakTiesRandomly() {
 return ((RandomTree) getClassifier()).getBreakTiesRandomly();
}

/**
 * Set the number of features to use in random selection.
 *
 * @param newNumFeatures Value to assign to numFeatures.
 */
public void setNumFeatures(int newNumFeatures) {
 ((RandomTree) getClassifier()).setKValue(newNumFeatures);
}

/**
 * Returns the tip text for this property
 *
 * @return tip text for this property suitable for displaying in the
 *         explorer/experimenter gui
 */
public String maxDepthTipText() {
 return ((RandomTree) getClassifier()).maxDepthTipText();
}

/**
 * Set the maximum depth of the tree, 0 for unlimited.
 *
 * @param value the maximum depth.
 */
public void setMaxDepth(int value) {
 ((RandomTree) getClassifier()).setMaxDepth(value);
}

/**
 * Returns the tip text for this property
 *
 * @return tip text for this property suitable for displaying in the
 *         explorer/experimenter gui
 */
public String numFeaturesTipText() {
 return ((RandomTree) getClassifier()).KValueTipText();
}

/**
 * Set whether to break ties randomly.
 *
 * @param newBreakTiesRandomly true if ties are to be broken randomly
 */
public void setBreakTiesRandomly(boolean newBreakTiesRandomly) {
 ((RandomTree) getClassifier()).setBreakTiesRandomly(newBreakTiesRandomly);
}

/**
 * Get whether to break ties randomly.
 *
 * @return true if ties are to be broken randomly.
 */
public boolean getBreakTiesRandomly() {
 return ((RandomTree) getClassifier()).getBreakTiesRandomly();
}

/**
 * Returns the tip text for this property
 *
 * @return tip text for this property suitable for displaying in the
 *         explorer/experimenter gui
 */
public String maxDepthTipText() {
 return ((RandomTree) getClassifier()).maxDepthTipText();
}

/**
 * Sets the seed for the random number generator.
 *
 * @param s the seed to be used
 */
public void setSeed(int s) {
 super.setSeed(s);
 ((RandomTree) getClassifier()).setSeed(s);
}

/**
 * Set the number of decimal places.
 */
public void setNumDecimalPlaces(int num) {
 super.setNumDecimalPlaces(num);
 ((RandomTree) getClassifier()).setNumDecimalPlaces(num);
}

/**
 * Set the preferred batch size for batch prediction.
 *
 * @param size the batch size to use
 */
@Override
public void setBatchSize(String size) {
 super.setBatchSize(size);
 ((RandomTree) getClassifier()).setBatchSize(size);
}

/**
 * Set debugging mode.
 *
 * @param debug true if debug output should be printed
 */
public void setDebug(boolean debug) {
 super.setDebug(debug);
 ((RandomTree) getClassifier()).setDebug(debug);
}