Extracts hidden correlations between high-dimensional datasets
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Updated
May 13, 2020 - Jupyter Notebook
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relevant-feature-analysis
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High data dimensionality and irrelevant features can negatively impact the performance of machine learning algorithms. This repository implements the Permutation feature importance method to enhance the performance of some machine learning models by identifying the contribution of each feature used.
feature-selection
classification
optimization-methods
decision-trees
predictive-modeling
support-vector-machines
optimization-algorithms
feature-importance
k-nearest-neighbors
permutation-importance
relevant-feature-analysis
wrapper-methods
feature-selection-methods
permutation-feature-importance
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Updated
Mar 12, 2024 - Jupyter Notebook
Opportunities and challenges in partitioning the graph measure space of real-world networks
machine-learning
social-networks
feature-selection
networkx
feature-extraction
networks
complex-networks
network-measures
network-analysis
igraph
affiliation-networks
ppi-networks
brain-networks
skewed-data
relevant-feature-analysis
network-classification
unbalanced-datasets
transportation-networks
web-networks
metabolic-networks
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Updated
Feb 1, 2021 - Jupyter Notebook
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