abalone
There are 34 repositories under abalone topic.
towzeur/gym-abalone
An environment of the board game Abalone using OpenAI's Gym API
nishitpatel01/predicting-age-of-abalone-using-regression
Predicting the age of abalone using multiple regression in R
Scriptim/Abalone-BoAI
A Python implementation of the board game Abalone intended to be played by artificial intelligence
sdbx/minda
online abalone board game
MaxInGaussian/GPoFM
GPoFM: Gaussian Process Training with Optimized Feature Maps for Shift-Invariant Kernels
nathanfallet/abalone
Simplified Abalone game for school
peso/el_boana
Winner of the 1993 Abalone competition at University of Waterloo
vincentfrochot/abalone-website
Website to play Abalone game online.
void4/pyabalone
A Python implementation of the board game Abalone
alahyaoui/Abalone
My Implementation of the game Abalone in C++
AmitStreit/AbaloneGameWPFcsharp
Abalone game in wpf/c#. With the possibility to play against human or computer agent
andi611/LibSVM-Classification
Performing classification tasks with the LibSVM toolkit on four different datasets: Iris, News, Abalone, and Income.
Clare-eDNA/eDNA_reflects_common_variation
Environmental DNA reflects common mitochondrial haplotypic variation
darshilmaru01/AbaloneClassification
Machine Learning Classification on Abalone Dataset
peso/abmove
AbMove is a library for the board game Abalone. It supports Unix and Windows
renanleonel/abalone-classification-regression
implementation of kNN, Decision Tree, Random Forest, and SVM algorithms for classification and regression applied to the abalone dataset.
semnan-university-ai/Abalone
Abalone Data Set
ablanco1950/ABALONE_DECISIONTREE_C4-5
ABALONE_DECISIONTREE_C4-5: A procedure is attached that uses the Abalone file (https://archive.ics.uci.edu/ml/datasets/abalone) as test and training . After evaluating the entropy of each field, a tree has been built with the nodes corresponding to fields 0, 7 and 4 and branch values ??in each node: 1 for the root node corresponding to field 0, 29 for the next node in the hierarchy corresponding to field 7, and 33 in the last node corresponding to field 4. The values ??of each field have been associated with indices, which can encompass several real values. the values ??of these indices are those that have been considered for the calculation of entropies and for making a branching of values ??at each node. A hit rate of around 58% is obtained, that is, in the low range of the existing procedures to treat this multiclass file, which are detailed in the documentation to download from https://archive.ics.uci.edu/ml/ datasets / abalone The depth of the tree has been increased without obtaining significant improvements. Nor has it been significantly improved by applying adaboost. Resources: Spyder 4 On the c: drive there should be the abalone-1.data file downloaded from https://archive.ics.uci.edu/ml/datasets/abalone Functioning: From Spyder run: AbaloneDecisionTree_C4-5-ThreeLevels.py The screen indicates the number of hits and failures and in the file C:\AbaloneCorrected.txt the records of the test file (records 3133 to 4177 of abalone-1.data) with an indication of whether their predicted class values ??coincide with the reals, the predicted class value and the order number of the record in abalone-1.data The following programs are also attached: AbaloneDecisionTree_ID3.py and AbaloneDecisionTree_C4-5_parameters.py that have served to calculate the necessary parameters to build the tree. Cite this software as: ** Alfonso Blanco García ** ABALONE_DECISIONTREE_C4-5 References: https://archive.ics.uci.edu/ml/datasets/abalone
ablanco1950/ABALONE_NAIVEBAYES_WEIGHTED_ADABOOST
ABALONE_NAIVEBAYES_WEIGHTED_ADABOOST: Two procedures are attached that use the Abalone file as test and training (https://archive.ics.uci.edu/ml/datasets/abalone). Both start from a treatment of the training part calculating the frequencies corresponding to each value of each field and applying a Naive Bayes probability calculation. In a second step, one of the procedures takes advantage of the previous result to apply weights based on each field to the wrong or true records. The other procedure uses Adaboost, using the adaboost routine published at https://github.com/jaimeps/adaboost-implementation (Jaime Pastor). A hit rate of around 58% is obtained, that is, in the low range of the existing procedures to treat this multiclass file, which are detailed in the documentation to download from https://archive.ics.uci.edu/ml/ datasets / abalone
DataThomas/xhec-mlops-project-student
The goal of this project was to get familiar with certain best practices and packages regarding MLOPS such as MLflow. For this purpose, we relied on the abalone age prediction Kaggle contest. This is a fork of a group project of my DSB Master's Degree at HEC Paris for our MLOPS course in cooperation with Artifact (https://www.artefact.com/)
dinabandhu50/ABALONE_PROJECT
This Repository contains machine learning classification projects
fnbalves/abalone
A study about the UCI's abalone dataset
Jules182/Abalone
Griffith College Dublin - HCI & GUI Programming - Project - Abalone
MelissaLaurino/PCA-Analysis
Blacklip Abalone (Haliotis rubra) PCA Analysis
MukundKal/abalone-age-regressor
Predicting the age of abalone from physical measurements.
rpatelpj/abalone-regression-study
Study on impact of different algorithms on regression models for abalone dataset.
simi27/Abalone-3rdYearProject
A third year group project.
ablanco1950/SKLEARN_HitRate_vs_Sensitivity
Using the Sklearn classifiers: Naive Bayes, Random Forest, Adaboost, Gradient Boost, Logistic Regression and Decision Tree good success rates are observed in a very simple manner. In this work sensitivity is also considered. Treating each record individually, differences are found in the results for each record depending on the model used, which is hidden in treatments that compute a total volume of data
dtroupe18/StatsFinalProject
Simple ML project using UCI dataset
koromodako/INSA-4IF-Prolog-Abalone
Abalone game developped using prolog and packaged with a nice web interface
noahgift/abalone_gender
Abalone Gender Classification
Pranav2106/Project-Abalone
The Abalone project is a Java-based implementation of the classic multiplayer board game. The game is designed to be played by 2 to 4 players and challenges players to use strategy and skill to maneuver their marbles on the game board with the goal of pushing their opponent's marbles off the edge.