mrmr
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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).
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Jun 9, 2018 - Python
Conformal Inference tools using python
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Apr 16, 2020 - Python
This is a complete guide on how to do Pyradiomics based feature extraction and then, build a model to calculate the grade of glioma.
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May 9, 2020 - Jupyter Notebook
Implementations of various feature selection methods
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Nov 30, 2020 - Python
Master MVA - Time Series Project
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May 16, 2021 - Jupyter Notebook
Maximum Relevance Minimum Redundancy for big datasets
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Sep 30, 2021 - Python
An improved implementation of the classical feature selection method: minimum Redundancy and Maximum Relevance (mRMR).
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Apr 1, 2022 - C++
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.
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May 5, 2022 - Scala
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.
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Aug 19, 2022 - Python
Feature engineering, selection and XGBoost modeling for the Kaggle House Prices Regression competition.
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Sep 22, 2022
Implementation of various feature selection methods using TensorFlow library.
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Nov 21, 2022 - Python
Some Hybrid Machine Learning Algorithms 🤖 that I developed during my 4th Semester 📓
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May 17, 2023 - Jupyter Notebook
Cardiovasular Disease Detection using Naive Bayes, Logistic Regression, Random Forest & Support Vector Machine, while comparing the Naive Bayes models with the rest. LIME was also used to explain the predictions of the model.
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Dec 13, 2023 - Jupyter Notebook
Diabetes Prediction using Three Machine Learning Algorithms - Logistic Regression, Random Forest & SVM
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Dec 24, 2023 - Python
All Relevant Feature Selection
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May 22, 2024 - Python
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May 28, 2024 - Jupyter Notebook
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