This repository contains Python scripts for calculating the Gini Impurity measure for each feature in a relational dataset, great for feature selection, data preprocessing, decision tree construction, binary classification tasks.
A python library for the computation of various concentration, inequality and diversity indices
Decision Tree classifier from scratch without any machine learning libraries
Decision Tree Implementation from Scratch
The complete Python code for the Attribute Selection Algorithm, using the parameters Entropy, Information gain and Gini Index. The following code will help in induction of the Decision Tree using a custom data set present in CSV format.
Decision Tree Classification via CART Algorithm
A collection of experiments I have performed for the course "Machine Learning" as part of the curriculum for Semester 6 of TY B. Tech. Computer Engineering at KJ Somaiya College of Engineering.
A comprehensive study of the "Balmer Peak" phenomenon in binary search algorithms, where introducing controlled randomness (epsilon) can improve performance on certain array structures.
A decision tree built in python for my 4th year module CA4010, Data Mining
Assignments and Project from NJIT CS 675
Graphical user interface for azapy library - Finacial Portfolio Optimization Algorithms
Inequality dashboard exploring Gini, Poverty Headcount Ratio, and Income Distribution data
2D Gini Index Implementation
These are coding assignments and projects for the CS 675 Machine Learning course.
Add a description, image, and links to the gini-index topic page so that developers can more easily learn about it.
To associate your repository with the gini-index topic, visit your repo's landing page and select "manage topics."