RandomForest Class

[SerializableAttribute]
public class RandomForest : MulticlassClassifierBase, 
	IParallel, ISupportsCancellation

<SerializableAttribute>
Public Class RandomForest
	Inherits MulticlassClassifierBase
	Implements IParallel, ISupportsCancellation

// Fix random seed for reproducibility
Accord.Math.Random.Generator.Seed = 1;

// In this example, we will process the famous Fisher's Iris dataset in 
// which the task is to classify weather the features of an Iris flower 
// belongs to an Iris setosa, an Iris versicolor, or an Iris virginica:
// 
//  - https://en.wikipedia.org/wiki/Iris_flower_data_set
// 

// First, let's load the dataset:
var iris = new DataSets.Iris();
double[][] inputs = iris.Instances; // flower features
int[] outputs = iris.ClassLabels; // flower categories

// Create the forest learning algorithm
var teacher = new RandomForestLearning()
{
    NumberOfTrees = 10, // use 10 trees in the forest
};

// Finally, learn a random forest from data
var forest = teacher.Learn(inputs, outputs);

// We can estimate class labels using
int[] predicted = forest.Decide(inputs);

// And the classification error (0.0006) can be computed as 
double error = new ZeroOneLoss(outputs).Loss(forest.Decide(inputs));

// Fix random seed for reproducibility
Accord.Math.Random.Generator.Seed = 1;

// This example uses the Nursery Database available from the University of
// California Irvine repository of machine learning databases, available at
// 
//   http://archive.ics.uci.edu/ml/machine-learning-databases/nursery/nursery.names
// 
// The description paragraph is listed as follows.
// 
//   Nursery Database was derived from a hierarchical decision model
//   originally developed to rank applications for nursery schools. It
//   was used during several years in 1980's when there was excessive
//   enrollment to these schools in Ljubljana, Slovenia, and the
//   rejected applications frequently needed an objective
//   explanation. The final decision depended on three subproblems:
//   occupation of parents and child's nursery, family structure and
//   financial standing, and social and health picture of the family.
//   The model was developed within expert system shell for decision
//   making DEX (M. Bohanec, V. Rajkovic: Expert system for decision
//   making. Sistemica 1(1), pp. 145-157, 1990.).
// 

// Let's begin by loading the raw data. This string variable contains
// the contents of the nursery.data file as a single, continuous text.
// 
var nursery = new DataSets.Nursery(path: localPath);
int[][] inputs = nursery.Instances;
int[] outputs = nursery.ClassLabels;

// Now, let's create the forest learning algorithm
var teacher = new RandomForestLearning(nursery.VariableNames)
{
    NumberOfTrees = 1,
    SampleRatio = 1.0
};

// Finally, learn a random forest from data
var forest = teacher.Learn(inputs, outputs);

// We can estimate class labels using
int[] predicted = forest.Decide(inputs);

// And the classification error (0) can be computed as 
double error = new ZeroOneLoss(outputs).Loss(forest.Decide(inputs));