random-forest-classifier-update

Random Forest

A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting.

Modeled after scikit-learn's RandomForestClassifier.

$3

``bash
$ npm install random-forest-classifier
`

$3

`javascript
var fs = require('fs'),
RandomForestClassifier = require('random-forest-classifier').RandomForestClassifier;

var data = [
{
"length":5.1,
"width":3.5,
"petal_length":1.4,
"petal_width":0.2,
"species":"setosa"
},
{
"length":6.5,
"width":3,
"petal_length":5.2,
"petal_width":2,
"species":"virginica"
},
{
"length":6.6,
"width":3,
"petal_length":4.4,
"petal_width":1.4,
"species":"versicolor"
}...
];

var testdata = [{
"length":6.3,
"width":2.5,
"petal_length":5,
"petal_width":1.9,
//"species":"virginica"
},
{
"length":4.7,
"width":3.2,
"petal_length":1.3,
"petal_width":0.2,
//"species":"setosa"
}...
];

var rf = new RandomForestClassifier({
n_estimators: 10
});

rf.fit(data, null, "species", function(err, trees){
//console.log(JSON.stringify(trees, null, 4));
var pred = rf.predict(testdata, trees);

console.log(pred);

// pred = ["virginica", "setosa"]
});
`

$3

#### Options

n_estimators: integer, optional (default=10) The number of trees in the forest.

example

`javascript
var rf = new RandomForestClassifier({
n_estimators: 20
});
`

##### rf.fit(data, features, target, function(err, trees){})

Build a forest of trees from the training set (data, features, target).

parameters

- data: training data array
- features: if null it defaults to all features except the target, otherwise it only uses the array of features passed
- target: the target feature

example
`javascript
var rf = new RandomForestClassifier({
n_estimators: 20
});

rf.fit(data, ["length", "width"], "species", function(err, trees){
console.log(JSON.stringify(trees, null, 4));
});
`

##### rf.predict(data, trees)

The predicted class of an input sample is computed as the majority prediction of the trees in the forest.

parameters

- data: input sample
- trees: the forest of trees outputted by rf.fit

example
`javascript
var rf = new RandomForestClassifier({
n_estimators: 20
});

rf.fit(data, ["length", "width"], "species", function(err, trees){

var pred = rf.predict(sample_data, trees);

console.log(pred);
// pred = ["virginica", "setosa"]
});
``