function(features, explain, withScores) {
		this.editFeatureValues(features, /*remove_unknown_features=*/true);
		var scoresVector = this.predict(features, /*averaging=*/true, explain);
		return multilabelutils.mapScoresVectorToMultilabelResult(scoresVector, explain, withScores, /*threshold=*/0);
	}
function(features, explain, withScores) {
		this.editFeatureValues(features, true);
		var scoresVector = this.predict(features, true, explain);
		return multilabelutils.mapScoresVectorToMultilabelResult(scoresVector, explain, withScores, 0);
	}
function ( features , explain , withScores ) { this . editFeatureValues ( features , /*remove_unknown_features=*/ true ) ; var scoresVector = this . predict ( features , /*averaging=*/ true , explain ) ; return multilabelutils . mapScoresVectorToMultilabelResult ( scoresVector , explain , withScores , /*threshold=*/ 0 ) ; }
Use the model trained so far to classify a new sample.

@param sample a hash {feature1: value1, feature2: value2, ...}.
@param explain - int - if positive, an "explanation" field, with the given length, should be added to the result.
@param withScores - boolean - if true, return an array of [class,score], ordered by decreasing order of score.

@return an array whose VALUES are classes.
Use the model trained so far to classify a new sample .