server/src/main/resources/eclairjs/mllib/tree/DecisionTree.js
function (strategy) {
        this.logger = Logger.getLogger("DecisionTree_js");
        var jvmObject;
        if (strategy instanceof Strategy) {
            jvmObject = new org.apache.spark.mllib.tree.DecisionTree(Utils.unwrapObject(strategy));
        } else if (strategy instanceof rg.apache.spark.mllib.tree.DecisionTree) {
            jvmObject = strategy;
        } else {
            throw "DecisionTree invalid constructor parameter"
        }

        JavaWrapper.call(this, jvmObject);

    }
function (strategy) {
        this.logger = Logger.getLogger("DecisionTree_js");
        var jvmObject;
        if (strategy instanceof Strategy) {
            jvmObject = new org.apache.spark.mllib.tree.DecisionTree(Utils.unwrapObject(strategy));
        } else if (strategy instanceof rg.apache.spark.mllib.tree.DecisionTree) {
            jvmObject = strategy;
        } else {
            throw "DecisionTree invalid constructor parameter"
        }

        JavaWrapper.call(this, jvmObject);

    }
function ( strategy ) { this . logger = Logger . getLogger ( "DecisionTree_js" ) ; var jvmObject ; if ( strategy instanceof Strategy ) { jvmObject = new org . apache . spark . mllib . tree . DecisionTree ( Utils . unwrapObject ( strategy ) ) ; } else if ( strategy instanceof rg . apache . spark . mllib . tree . DecisionTree ) { jvmObject = strategy ; } else { throw "DecisionTree invalid constructor parameter" } JavaWrapper . call ( this , jvmObject ) ; }
A class which implements a decision tree learning algorithm for classification and regression.
It supports both continuous and categorical features.
@param strategy The configuration parameters for the tree algorithm which specify the type
of algorithm (classification, regression, etc.), feature type (continuous,
categorical), depth of the tree, quantile calculation strategy, etc.
@classdesc
 
@param {module:eclairjs/mllib/tree/configuration.Strategy} strategy
@class
@memberof module:eclairjs/mllib/tree
A class which implements a decision tree learning algorithm for classification and regression . It supports both continuous and categorical features .