AutoViML/featurewiz

Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.

Language: Python - Size: 10.6 MB - Last synced at: 22 days ago - Pushed at: 4 months ago - Stars: 648 - Forks: 96

ThomasBury/arfs

All Relevant Feature Selection

Language: Python - Size: 77.6 MB - Last synced at: about 1 month ago - Pushed at: 3 months ago - Stars: 134 - Forks: 14

benhorvath/sklearn-mrmr

scikit-learn compatible MRMR feature selection

Language: Jupyter Notebook - Size: 770 KB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 2 - Forks: 1

ArchismwanChatterjee/Parkinson_Detection

Language: Jupyter Notebook - Size: 9.94 MB - Last synced at: 3 months ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

smzoha/diabetes-prediction

Diabetes Prediction using Three Machine Learning Algorithms - Logistic Regression, Random Forest & SVM

Language: Python - Size: 25.4 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

tlatkowski/tf-feature-selection

Implementation of various feature selection methods using TensorFlow library.

Language: Python - Size: 33.2 KB - Last synced at: 2 months ago - Pushed at: over 2 years ago - Stars: 9 - Forks: 3

qjyimyy/XD-machine-learing

Language: Python - Size: 1.2 MB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 4 - Forks: 0

kr-prince/mRMR

This is an App developed in Python to implement the algorithm for minimum redundancy maximum ralevance. The formulation was based on a research paper from Chris Ding and Hanchuan Peng (Minimum Redundancy Feature Selection from Microarray Gene Expression Data).

Language: Python - Size: 8 MB - Last synced at: over 1 year ago - Pushed at: about 7 years ago - Stars: 12 - Forks: 3

jvicentem/big_mrmr

Maximum Relevance Minimum Redundancy for big datasets

Language: Python - Size: 60.5 KB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 5 - Forks: 1

sramirez/fast-mRMR

An improved implementation of the classical feature selection method: minimum Redundancy and Maximum Relevance (mRMR).

Language: C++ - Size: 4.33 MB - Last synced at: over 1 year ago - Pushed at: about 3 years ago - Stars: 80 - Forks: 25

sramirez/spark-infotheoretic-feature-selection

This package contains a generic implementation of greedy Information Theoretic Feature Selection (FS) methods. The implementation is based on the common theoretic framework presented by Gavin Brown. Implementations of mRMR, InfoGain, JMI and other commonly used FS filters are provided.

Language: Scala - Size: 10.8 MB - Last synced at: over 1 year ago - Pushed at: about 3 years ago - Stars: 133 - Forks: 49

helenzhao093/MLMethods

Implementations of various feature selection methods

Language: Python - Size: 59.6 KB - Last synced at: almost 2 years ago - Pushed at: over 4 years ago - Stars: 18 - Forks: 7

BCImonk/Hybrid_Machine_Learning_Algorithms

Some Hybrid Machine Learning Algorithms :robot: that I developed during my 4th Semester :notebook:

Language: Jupyter Notebook - Size: 658 KB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0

d-dawg78/MVA_ST

Master MVA - Time Series Project

Language: Jupyter Notebook - Size: 2.51 MB - Last synced at: 10 days ago - Pushed at: about 4 years ago - Stars: 1 - Forks: 1

Sudhir22/conformalInference

Conformal Inference tools using python

Language: Python - Size: 43 KB - Last synced at: 6 months ago - Pushed at: about 5 years ago - Stars: 3 - Forks: 0

AhmetZamanis/Kaggle-House-Prices-Regression-FeatureEng

Feature engineering, selection and XGBoost modeling for the Kaggle House Prices Regression competition.

Size: 570 KB - Last synced at: 29 days ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

SupernovaSatsangi23/Modifying-Biomarker-Gene-Identification-For-Effective-Cancer-Categorization

A project that focuses on implementing a hybrid approach that modifies the identification of biomarker genes for better categorization of cancer. The methodology is a fusion of MRMR filter method for feature selection, steady state genetic algorithm and a MLP classifier.

Language: Python - Size: 6.84 KB - Last synced at: 3 months ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0

naveenshukla/feature-selection-in-spark

Feature selection in Apache Spark using Minimum Redundancy and Maximum Relevance

Language: Python - Size: 43.9 KB - Last synced at: over 2 years ago - Pushed at: over 7 years ago - Stars: 2 - Forks: 1