Decision tree javascript

Decision tree javascript
Techiio-author
Written by Sagar RabidasDecember 13, 2021
7 min read
JavaScript
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Techiio-author
Sagar Rabidas

Software Developer

In this blog, we will discuss Decision tree javascript.

Introduction to Decision tree javascript

Decision tree javascript is a tool this is utilized by software program developers to construct analytics equipment that helps users or customers to visualize choices and to recognize the results of the effects of that choice. Like different trees in laptop technological know-how, the selection tree additionally has nodes. These nodes are named selection nodes and outcome nodes, final results nodes also are known as stop nodes. In certain conditions, the selection tree additionally has threat nodes that desire one group of effects over other businesses of results under some unique situations. The selection tree in javascript is part of the diagram library on javascript.

Syntax of Decision Tree:

var decisionTree =
new Case( true, Array(
new Case ( function(n){ return n < 0; }, Math.sin),
new Case ( function(n){ return n < 2; }, “0 <= n < 2” ),
new Case ( true, ‘Greater than two” )));
decisionTree.evaluate(1);
decisionTree.evaluate(-Math.PI/2);
decisionTree.evaluate(5);

Using this implementation, you can arbitrarily nest your tree:

Case.prototype = {
nomatch : { match : false },
match : function (v) { return  { match : true , result : v }; },
evaluate : function( object ) {
var match = this.predicate;
if ( match instanceof Function )
match = match( object );
if ( match ) {
if (this.action instanceof Function )
return this.match( this.action(object) );
if ( this.action instanceof Case )
return this.action.evaluate( object );
if ( this.action instanceof Array ) {
var decision;
var result;
for (var c = 0; c < this.action.length; c++ ) {
decision = this.action[c];
if ( decision instanceof Case ) {
result = decision.evaluate( object );
if (result.match)
return result;
} else throw("Array of Case expected");
}
return this.nomatch;
}
return this.match(this.action);
}
return this.nomatch;
}
};

Example of Decision Tree Javascript:-

// decision tree API
const decision = (conditionFunction, trueOutcome, falseOutcome) =>
(context) => conditionFunction(context) ? trueOutcome : falseOutcome;
const decide = (context, decision) => “{
const outcome  = decision (context);
return typeof outcome === “function” ? decide(context, outcome) : outcome;
}
// Example Code
const isPositive = x => x > 0;
const isNegative = x => x < 0;
const isZero = x => x === 0;
const numberSignDecision =
decision( isPositive, “+”, decision(isNegative, “-”, decision(isZero, “0”, ?)));
const contextValues = [“number”, 1, 0, -1, Number.NaN, ];
for (const value of contextValues) {
console.log(value, decide(value, numberSignDecision));
}

Advantages of Decision Tree

  • Decision Trees are easily understandable even by non-technical people and also easily interpretable.
  • Decision Trees can work efficiently even with little data and as well as Big Data.
  • Decision Trees use the model of the white box that means if a given result is provided by a model the explanation for the result is easily replicated by simple mathematics formulae.
  • It can be solved by combining with other decision techniques such as brainstorming and other techniques.

Disadvantages of Decision Tree

  • Most of the algorithms like ID3 and C4.5 require that the target attribute will have only discrete values.
  • As decision trees use the “divide and conquer” method, they tend to perform well if a few highly relevant attributes exist, but less so if many complex interactions are present.
  • The greedy characteristic of decision trees leads to another disadvantage that should be pointed out. This is its over-sensitivity to the training set, irrelevant attributes, and noise.

Conclusion:-

Based on this article, we have found out that decision trees work on the conditions and give the results according to that condition. We also can make a decision tree by using if-else statements after adding traversing in these steps. Machine Learning developers use mostly decision trees because it is easy to understand and interpret. By using a decision tree, users can take decisions easily based on preassumed conditions without any hesitation. And along with this decision trees are self-explanatory and easy to follow. In simpler words, if a decision tree has a countable and reasonable number of leaves then a non-technical user can also understand it. A decision tree has more advantages than its disadvantages so learn it and use it.

javascript
jquery
reactjs
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