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permutation-importance

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Feature importance refers to a measure of how important each feature/variable is in a dataset to the target variable or the model performance. It can be used to understand the relationships between variables and can also be used for feature selection to optimize the performance of machine learning models.

  • Updated Jun 18, 2023
  • Python

Developed a machine learning model using scikit-learn, implementing ensemble techniques, PCA, correlation analysis, and extensive feature engineering. The goal was to classify documents as either human-generated (0) or AI-generated (1) based on document embeddings, word count, and punctuation.

  • Updated Jun 15, 2024
  • Jupyter Notebook

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.

  • Updated Mar 12, 2024
  • Jupyter Notebook

This project aims to study the influence factors of international students' mobility with the case of international students from B&R countries studying in China.

  • Updated Mar 1, 2024
  • HTML

This project contains the data, code and results used in the paper title "On the relationship of novelty and value in digitalization patents: A machine learning approach".

  • Updated Jul 13, 2022
  • Python

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