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…

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

Adaptive Reinforcement Learning of curious AI basketball agents

In this project, we used 3 different metrics (Information Gain, Mutual Information, Chi Squared) to find important words and then we used them for the classification task. We compared the result at the end.

Feature Selection for Credit Scoring using Genetic Algorithm Wrapper(Information Gain)

q statistical learning

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.

"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.

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…

Implementing decision tree using ID3 algorithm based on Information Gain and using post pruning for improving accuracy

Applying different machine learning algorithms on PCGA Prostate Cancer Gene Dataset for Feature Selection, Dimensional Reduction and Classification and Regression

Visualisation for id3 classification algorithm.

Experimental classification algorithms on german credit data implemented using scikit-learn library

To apply decision tree classifier on iris dataset

Implementation of Decision tree learning algorithm with chi-square pruning

IUB CSE 425 (Artificial Intelligence)

Implementing your own decision tree algorithms and applying them to real world data!

Design and Implementation of Random Forest algorithm from scratch to execute Pacman strategies and actions in a deterministic, fully observable Pacman Environment.

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

decision trees made easy