ml-random-forest 2.1.0 | Documentation

ml-random-forest

2.1.0

RandomForestBase ▸
- Instance members
- #featureImportance
- #predict
- #predictionValues
- #predictOOB
- #selection
- #toJSON
- #train
RandomForestClassifier ▸
- Static members
- .load
- Instance members
- #getConfusionMatrix
- #predictProbability
- #selection
- #toJSON
RandomForestRegression ▸
- Static members
- .load
- Instance members
- #selection
- #toJSON

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RandomForestBase

src/RandomForestBase.js

new RandomForestBase()

Instance Members

▸ featureImportance()

src/RandomForestBase.js

Evaluate the feature importances for each tree in the ensemble

featureImportance(): Array

Returns

Array: feature importances

▸ predict(toPredict)

src/RandomForestBase.js

Predicts the output given the matrix to predict.

predict(toPredict: (Matrix | Array)): Array

Parameters

toPredict ((Matrix | Array))

Returns

Array: predictions

▸ predictionValues(toPredict)

src/RandomForestBase.js

Predicts the output given the matrix to predict.

predictionValues(toPredict: (Matrix | Array)): MatrixTransposeView

Parameters

toPredict ((Matrix | Array))

Returns

MatrixTransposeView: predictions of estimators

▸ predictOOB()

src/RandomForestBase.js

Returns the Out-Of-Bag predictions.

predictOOB(): Array

Returns

Array: predictions

▸ selection(values)

src/RandomForestBase.js

Method that returns the way the algorithm generates the predictions, for example, in classification you can return the mode of all predictions retrieved by the trees, or in case of regression you can use the mean or the median.

selection(values: Array): number

Parameters

values (Array) predictions of the estimators.

Returns

number: prediction.

▸ toJSON()

src/RandomForestBase.js

Export the current model to JSON.

toJSON(): object

Returns

object: Current model.

▸ train(trainingSet, trainingValues)

src/RandomForestBase.js

Train the decision tree with the given training set and labels.

train(trainingSet: (Matrix | Array), trainingValues: Array)

Parameters

trainingSet ((Matrix | Array))

trainingValues (Array)

RandomForestClassifier

src/RandomForestClassifier.js

new RandomForestClassifier()

Extends RandomForestBase

Static Members

▸ load(model)

src/RandomForestClassifier.js

Load a Decision tree classifier with the given model.

load(model: object): RandomForestClassifier

Parameters

model (object)

Returns

RandomForestClassifier:

Instance Members

▸ getConfusionMatrix()

src/RandomForestClassifier.js

Returns the confusion matrix Make sure to run train first.

getConfusionMatrix(): object

Returns

object: Current model.

▸ predictProbability(toPredict, label)

src/RandomForestClassifier.js

Predicts the probability of a label given the matrix to predict.

predictProbability(toPredict: (Matrix | Array), label: number): Array

Parameters

toPredict ((Matrix | Array))

label (number)

Returns

Array: predictions

▸ selection(values)

src/RandomForestClassifier.js

retrieve the prediction given the selection method.

selection(values: Array): number

Parameters

values (Array) predictions of the estimators.

Returns

number: prediction

▸ toJSON()

src/RandomForestClassifier.js

Export the current model to JSON.

toJSON(): object

Returns

object: Current model.

RandomForestRegression

src/RandomForestRegression.js

new RandomForestRegression()

Extends RandomForestBase

Static Members

▸ load(model)

src/RandomForestRegression.js

Load a Decision tree classifier with the given model.

load(model: object): RandomForestRegression

Parameters

model (object)

Returns

RandomForestRegression:

Instance Members

▸ selection(values)

src/RandomForestRegression.js

retrieve the prediction given the selection method.

selection(values: Array): number

Parameters

values (Array) predictions of the estimators.

Returns

number: prediction

▸ toJSON()

src/RandomForestRegression.js

Export the current model to JSON.

toJSON(): object

Returns

object: Current model.