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This code repository contains implementations of Decision Trees and Random Forests in Kotlin for both classification and regression tasks. Decision Trees and Random Forests are powerful machine learning algorithms for decision-making and prediction problems. The code is written in Kotlin, a modern and concise programming language.

Kotlin 100.00%
algorithms cpp forests gradle hashmap java javascript js kotlin math random tree

decision-trees-and-random-forests's Introduction

Decision Trees in Kotlin

This repository contains an implementation of Decision Trees in Kotlin for classification and regression tasks.

Prerequisites

  • Kotlin 1.4 or higher
  • Gradle 4.10 or higher

Getting Started

Clone this repository and build the project using Gradle: gradle build

Usage

import com.example.decisiontree.DecisionTree

// Initialize a decision tree model for binary classification
val decisionTree = DecisionTree(maxDepth = 10)

// Train the model on a training dataset
decisionTree.fit(trainFeatures, trainLabels)

// Predict on a test dataset
val predictions = decisionTree.predict(testFeatures)

##Contributing
We welcome contributions to improve this implementation of Decision Trees in Kotlin. Please submit a pull request if you have any changes to suggest.

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