hahaha6

hahaha6's Stars

• QSCTech/zju-icicles

  浙江大学课程攻略共享计划

  Language:HTML36.6k1.1k749.4k

• PKUanonym/REKCARC-TSC-UHT

  清华大学计算机系课程攻略 Guidance for courses in Department of Computer Science and Technology, Tsinghua University

  Language:HTML32.8k887317.6k

• lib-pku/libpku

  贵校课程资料民间整理

  Language:TeX30.2k1.2k418.3k

• CSSEGISandData/COVID-19

  Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE

  29.1k8653.8k18.5k

• acheong08/ChatGPT

  Reverse engineered ChatGPT API

  Language:Python28k2908104.5k

• GaiZhenbiao/ChuanhuChatGPT

  GUI for ChatGPT API and many LLMs. Supports agents, file-based QA, GPT finetuning and query with web search. All with a neat UI.

  Language:Python15.1k857752.3k

• iamseancheney/python_for_data_analysis_2nd_chinese_version

  《利用Python进行数据分析·第2版》

  7.6k171102.6k

• CTeX-org/lshort-zh-cn

  A Chi­nese edi­tion of the Not So Short Introduction to LaTeX2ε

  Language:TeX2.9k4063398

• lydrainbowcat/tedukuri

  《算法竞赛进阶指南》资源社区

  Language:Roff2.2k8370603

• jundongl/scikit-feature

  open-source feature selection repository in python

  Language:Python1.5k6055449

• yiyandaoren/DataAnalysisInAction

  (Finished) Geek Time Data Analysis Practical 45 Lecture - Detailed notes containing markdown images mind map code data can be read directly code test

  Language:Python6943515277

• duxuhao/Feature-Selection

  Features selector based on the self selected-algorithm, loss function and validation method

  Language:Python6692313202

• analyticalmindsltd/smote_variants

  A collection of 85 minority oversampling techniques (SMOTE) for imbalanced learning with multi-class oversampling and model selection features

  Language:Jupyter Notebook6121634138

• kaushalshetty/FeatureSelectionGA

  Feature Selection using Genetic Algorithm (DEAP Framework)

  Language:Python3609995

• mixOmicsTeam/mixOmics

  Development repository for the Bioconductor package 'mixOmics '

  Language:R151921951

• ctlab/ITMO_FS

  Feature selection library in python

  Language:Python142101038

• stavskal/ADASYN

  Adaptive Synthetic Sampling Approach for Imbalanced Learning

  Language:Python1257127

• felix-last/kmeans_smote

  Oversampling for imbalanced learning based on k-means and SMOTE

  Language:Python1165958

• weepon/feature_selection

  常用的特征选择方法

  Language:Jupyter Notebook651015

• huangwenyi10/springmvc-mybatis-book

  Language:Java633166

• Kensuke-Mitsuzawa/DocumentFeatureSelection

  A set of metrics for feature selection from text data

  Language:Python4562412

• Nekooeimehr/MATLAB-Source-Code-Oversampling-Methods

  This repository contains the source code for four oversampling methods that I wrote in MATLAB: 1) SMOTE 2) Borderline SMOTE 3) Safe Level SMOTE 4) ASUWO (Adaptive Semi-Unsupervised Weighted Oversampling)

  Language:Matlab372117

• krishnadulal/Feature-Selection-in-Machine-Learning-using-Python-All-Code

  Feature Selection in Machine Learning using Python All Code

  Language:Jupyter Notebook342042

• syanga/pycit

  (Conditional) Independence testing & Markov blanket feature selection using k-NN mutual information and conditional mutual information estimators. Supports continuous, discrete, and mixed data, as well as multiprocessing.

  Language:Python20135

• wangjiwu/BreastTissue_classify_matlab

  使用libsvm进行分类

  Language:MATLAB17109

• fischlerben/Machine-Learning-Credit-Risk

  Machine-Learning project that uses a variety of credit-related risk factors to predict a potential client's credit risk. Machine Learning models include Logistic Regression, Balanced Random Forest and EasyEnsemble, and a variety of re-sampling techniques are used (Oversampling/SMOTE, Undersampling/Cluster Centroids, and SMOTEENN) to re-sample the data. Evaluation metrics like the accuracy score, classification report and confusion matrix are generated to compare models and determine which suits this particular set of data best.

  Language:Jupyter Notebook10202

• Claudmj/Microarray-analysis-with-Bayesian-networks

  Microarray analysis with Bayesian hierarchical clustering and Bayesian network clustering on three microarray datasets. Pearson's correlation coefficient and an augmented Markov blanket are used for feature selection.

  Language:Python6103

• GjjvdBurg/PyGenSVM

  Python package for the GenSVM classifier

  Language:Python5543

• 13VS/pso-gene-selection

  This project makes use of a generic algorithm Particle Swarm Optimization for selection of few informative features from a large dataset.

  Language:Python1101

• zbn123/cluster-smote

  Language:Python1100