Decision Tree Classifier

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C++ implementation of Decision Tree Classifier and Random Boosted Forest Classifier

Table of Contents

• About
• Getting Started
• Usage
• Built Using
• Authors

About

The implementation of predicting the occupancy status of the room. The accuracy of the prediction of occupancy in an office room using data from light, temperature, humidity and CO2 sensors has been evaluated with different statistical classification models like Decision Tree Classifier, Random Forest and Boosted Ran- dom Forest Classifier.Three data sets from the UCI Machine Learning Repository were used in this work, one for training and two for testing the models. The results from the various experiments show that a proper selection of features together with an appropriate classification model can have a significant impact on the accuracy prediction of the occupancy status of the room. Typically, the best accuracy is obtained from training

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

Required tools and packages on a linux system.

g++ : 5.5.0 (or) 7.4.0
python : 3.5.2
Dataset : https://archive.ics.uci.edu/ml/machine-learning-databases/00357/

Get the repository

git clone https://github.com/vpatel95/decision-tree-classifier.git

Installing

Following steps will get a development env running.

cd decision-tree-classifier
make

OR

cd decision-tree-classifier
g++ -std=c++11 -w -O3 app.cpp decision_tree.cpp -o app

Usage

Configurations of training a model is set by a config file located in the configs directory. The attributes in the config file are

Attribute	Sub values	Value(s)
classification_model	-	random_forest (or) decision_tree>
feature_set	-	[ <array_of_attributes> [ array_of_attributes ] ]
preprocessed_data	test	relative location of test file
	train	relative location of train file
	validation	relative location of validation file
extracted_data	attributes	relative location of attributes information file
	test	relative location of extracted test file
	train	relative location of extracted train file
	validation	relative location of extracted validation file
bag_size_percent	-	percentage of data to be used from training set for bagging
num_trees	-	number of trees for random forest
boosting	-	true (or) false
verbosity	-	1 (or) 2 (or) 3
display_trees	-	true (or) false

Deployment

Add additional notes about how to deploy this on a live system.

Built Using

• C++
• Python

✍️ Authors

• @vpatel95