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

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

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.

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

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…

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

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

Python implementation of the ID3 Decision Tree algorithm from scratch, including entropy, information gain, recursive tree induction, and classification of categorical datasets.

Implementation of Decision tree learning algorithm with chi-square pruning

Camera-ready repository for REIGN, accepted at the MLGenX 2026 ICLR Workshop, on adaptive experiment design with hierarchical transfer and information gain.

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

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

decision trees made easy

Custom implementation of Naïve Bayes and Decision Tree classifiers from scratch, applied to categorical datasets (Mushroom & Congressional Voting). No scikit-learn used—focus on core ML principles like entropy, Laplace smoothing, and tree traversal.

A project of my course "Introduction to Pattern Recognition". Realize Decision Tree algorithm using PYTHON.

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

[studies] Clustering with evolution strategy, done in scope of the subject "Evolutionary Methods and Machine Learning"

Thyroid cancer recurrence classification with XGBoost, Information Gain feature selection, K-Fold CV, grid search, and comprehensive metrics.

Comparative analysis of filter, wrapper, and embedded feature selection methods for efficient machine learning and intrusion detection using SVM on large-scale cybersecurity datasets.

Focused on math and applied the methods into programming.