gini-impurity
There are 15 repositories under gini-impurity topic.
ValentinRicher/understanding-decision-trees
This notebook can be downloaded, tested and modified with Google Colab and aims at explainable how a Decision Tree is built. It is also coupled with a Medium article.
akshay-madar/decisionTree-from-scratch
Implemented decision tree algo. from scratch by calculating gini impurity and information gain for performing splits
irutupatel/Decision-Tree
A Decision Tree; multi-class classification problem with continuous feature/attribute values
Providence-Nate/RandomForests-PacMan
Design and Implementation of Random Forest algorithm from scratch to execute Pacman strategies and actions in a deterministic, fully observable Pacman Environment.
karthikvadlamani/doordash-delivery-predictions
Prediction of delivery times for DoorDash deliveries. Performed feature engineering (creation, encoding), feature selection using (multi)collinearity analysis, Gini importance and PCA. Applied 6 ML models to perform regression analysis on delivery time prediction.
MJawad-AbouAleiwi/SD201
The practical works (TP) of SD201 - Mining of Large Datasets course at Télécom Paris.
spartan-minhbui/decisiontree
Decision Tree Implementation.
vicaaa12/machine-learning
machine learning
akash18tripathi/Decision-Trees-implementation-from-scratch
This repository contains an implementation of the Decision Tree algorithm from scratch using various impurity methods such as Gini index, entropy, misclassification error, etc.
celuk/ml-decision-tree-classifier
Machine Learning Decision Tree Classifier with Gini Algorithm written from scratch
dhawal777/DecisionTree
Implementation of decision tree from scratch along with analysis of its performance with different types of impurity measures
matteorigat/Tree-predictors-binary-classification-project
Tree Predictors for Binary Classification for Secondary Mushroom dataset
noiseOnTheNet/post017_binary_decision_tree
Second step in decision tree building
Saba-Gul/Comparative-Study-of-Predicting-Students-Adaptability-Level-in-Online-Education
Comparative Study of KNN and Decision Tree Models for Predicting Students' Adaptability Level in Online Education
secondlevel/Decision-Tree
Pattern Recognition homework3 in NYCU. This project is to implement the Decision Tree, AdaBoost and Random Forest algorithm by using only NumPy.