AIKA is a new type of artificial neural network designed to more closely mimic the behavior of a biological brain and to bridge the gap to classical AI. A key design decision in the Aika network is to conceptually separate the activations from their neurons, meaning that there are two separate graphs. One graph consisting of neurons and synapses…

Content: Root node, Decision node & Leaf nodes, Attribute Selection Measure (ASM), Feature Importance (Information Gain), Gini index

An innovative Python implementation of decision trees for machine learning, showcasing algorithmic learning from scratch with practical examples and a focus on AI principles.

q statistical learning

Tech Challenge of the Postgraduate in Data Analytics, from FIAP, analyzing Brent Oil price data, in comparison with historical, economic and societal data, integrating correlation and causality analyzes of items with prices, as well as developing a model forecast and an importance analysis through information gain from a forest model (XGBoost)

Delved into advanced techniques to enhance ML performance during the uOttawa 2023 ML course. This repository offers Python implementations of Naïve Bayes (NB) and K-Nearest Neighbor (KNN) classifiers on the MCS dataset.

Visualisation for id3 classification algorithm.

This repository contains the assignments of Artificial Intelligence course at Sharif University of Technology.

This project focuses on implementing and analyzing the learning process in decision trees using Connect 4.

A repository containing the source code, datasets, and ranked features for the Nested Bigrams method proposed in a paper published in ICDMW. This method is designed for authorship attribution in source code to address cybersecurity issues.

Information gain can be used to get information about the value of attributes regarding a conceived result.

"A set of Jupyter Notebooks on feature selection methods in Python for machine learning. It covers techniques like constant feature removal, correlation analysis, information gain, chi-square testing, univariate selection, and feature importance, with datasets included for practical application.

This is a Decision Tree implementation with Python which uses information gain to split attributes. It does not use any ML library.

Learning informed sampling distributions and information gains for efficient exploration planning.

Implementation of classic machine learning concepts and algorithms from scratch and math behind their implementation.Written in Jupiter Notebook Python

Information gain of a car dataset was calculated in this notebook

Polycystic Ovary Syndrome (PCOS) is a widespread pathology that affects many aspects of women's health, with long-term consequences beyond the reproductive age. The wide variety of clinical referrals, as well as the lack of internationally accepted diagnostic procedures, have had a significant impact on making it difficult to determine the exact…

Polycystic Ovary Syndrome (PCOS) is a widespread pathology that affects many aspects of women's health, with long-term consequences beyond the reproductive age. The wide variety of clinical referrals, as well as the lack of internationally accepted diagnostic procedures, have had a significant impact on making it difficult to determine the exact…

Implemented a Decision Tree from Scratch using binary univariate split, entropy, and information gain. Used Gini index and Pruning for performance improvement.