GitHub topics: abalone

Repositories

hannah-doran/sci_diving_2025_DATA271

Final Project for DATA271, Dr. Johnson, Spring 2025, Cal Poly Humboldt

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jonathanaguirre/Abalone-Ring-Prediction

Predict rings of White Abalone via length, diameter, and height.

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Mohita21/AbaloneDataSet

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simi27/Abalone-3rdYearProject

A third year group project.

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MukundKal/abalone-age-regressor

Predicting the age of abalone from physical measurements.

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semnan-university-ai/Abalone 📦

Abalone Data Set

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nathanfallet/abalone 📦

Simplified Abalone game for school

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Scriptim/Abalone-BoAI

A Python implementation of the board game Abalone intended to be played by artificial intelligence

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MelissaLaurino/PCA-Analysis

Blacklip Abalone (Haliotis rubra) PCA Analysis

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alahyaoui/Abalone

My Implementation of the game Abalone in C++

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MaxInGaussian/GPoFM

GPoFM: Gaussian Process Training with Optimized Feature Maps for Shift-Invariant Kernels

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Jules182/Abalone

Griffith College Dublin - HCI & GUI Programming - Project - Abalone

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towzeur/gym-abalone

An environment of the board game Abalone using OpenAI's Gym API

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koromodako/INSA-4IF-Prolog-Abalone Fork of antitoine/INSA-4IF-Prolog-Abalone 📦

Abalone game developped using prolog and packaged with a nice web interface

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sdbx/minda

online abalone board game

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renanleonel/abalone-classification-regression

implementation of kNN, Decision Tree, Random Forest, and SVM algorithms for classification and regression applied to the abalone dataset.

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nishitpatel01/predicting-age-of-abalone-using-regression

Predicting the age of abalone using multiple regression in R

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vincentfrochot/abalone-website

Website to play Abalone game online.

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peso/el_boana

Winner of the 1993 Abalone competition at University of Waterloo

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peso/abmove

AbMove is a library for the board game Abalone. It supports Unix and Windows

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Clare-eDNA/eDNA_reflects_common_variation

Environmental DNA reflects common mitochondrial haplotypic variation

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darshilmaru01/AbaloneClassification

Machine Learning Classification on Abalone Dataset

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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

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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

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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

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abalone 34 machine-learning 8 game 5 board-game 4 regression 4 linear-regression 4 abalone-dataset 3 classification-algorithm 3 python 3 random-forest 3 classification 3 decision-tree 3 svm 2 python3 2 ai 2 logistic-regression 2 adaboost 2 ml 2 abalone-data-set 2 jupyter-notebook 2 naive-bayes 2 sklearn 2 cpp 2 uci-machine-learning 2 svm-classifier 2 marine-biology 2 scikit-learn 2 libsvm 1 populationgenetics 1 paua 1 environmentaldna 1 edna 1 library 1 typescript 1 nextjs 1 rmarkdown 1 regression-analysis 1 regression-algorithms 1 r 1 multilinear-regression 1 libsvm-ready 1 news-dataset 1 knn 1 games 1 newsgroups-dataset 1 c45-trees 1 bot 1 csharp 1 susy 1 wpf 1 svm-training 1 hastie 1 exploratory-data-analysis 1 gradient-boosting 1 model-evaluation 1 data-mining 1 random 1 neural-network 1 income 1 income-dataset 1 knn-classification 1 iris 1 mathplotlib 1 iris-dataset 1 prolog-implementation 1 github 1 git 1 smote 1 decision-trees 1 xgboost 1 randomforestregressor 1 prediction 1 experiment 1 urchins 1 urchin-barren 1 urchin 1 surveys 1 regime-shifts 1 regime 1 reefcheckcalifornia 1 reefcheck 1 reef 1 kelp-forest 1 kelp 1 ecology 1 coastal-ecology 1 california 1 biodiversity 1 reinforcement-learning 1 open-ai 1 gym 1 java 1 hci 1 gui 1 game-development 1 optimization 1 gaussian-processes 1 boston-housing 1 qtdesigner 1 qtcreator 1