A Python Package that computes Target Permutation Importances (Null Importances) of a machine learning model.
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Apr 3, 2024 - Python
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permutation-importance
Here are 18 public repositories matching this topic...
eXplainable Artificial Intelligence (XAI) Basic Algorithms on Iris Dataset
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Jan 5, 2024 - Python
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.
data-science machine-learning sql random-forest linear-regression data-visualization naive-bayes-classifier decision-tree-classifier kneighborsclassifier partial-dependence-plot push-pull auc-roc-curve permutation-importance ice-plot
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Mar 1, 2024 - HTML
End-to-end project to analyze and model concrete compressive strength data then productionize the best model to help civil engineers determine concrete structural integrity
python flask data-science regression eda xgboost pca pickle concrete feature-engineering hyperparameter-tuning civil-engineering permutation-importance compressive-strength
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Jun 20, 2023 - Python
Klasifikasi Musik Berdasarkan Genre Menggunakan Metode Naive Bayes.
feature-extraction naive-bayes-classifier classification music-classification mel-frequency-cepstral-coefficients permutation-importance
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May 25, 2024 - Python
This repo is all about feature importance. Whereby we look at the ways one can identify if a feature is worth having in the model or rather if it has a significant influence in the prediction. The methods are model-agnostic.
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Apr 23, 2024 - HTML
These training sessions in machine learning, conducted by Yandex, are dedicated to classical machine learning. This offers an opportunity to reinforce theoretical knowledge through practice on training tasks.
decision-tree ensemble-model linear-models boosting bagging regularization-methods permutation-importance classical-machine-learning
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Feb 10, 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.
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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Mar 12, 2024 - Jupyter Notebook
This project fits and tunes several regression models to predict Parkinson's symptom severity scores from voice recordings.
python machine-learning random-forest linear-regression sklearn jupyter-notebook pandas data-visualization seaborn xgboost regularization feature-engineering regression-models decision-tree hyperparameter-tuning gradient-boosting-regressor permutation-importance feature-analysis
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Mar 30, 2022 - Jupyter Notebook
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".
machine-learning scikit-learn machine-learning-algorithms machinelearning patents machine-learning-models partial-dependence-plot machinelearning-python patentsview machine-learning-interpretability permutation-importance kpss machine-learning-interpretation
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Jul 13, 2022 - Python
A solid foundational understanding of XAI, primarily emphasizing how XAI methodologies can expose latent biases in datasets and reveal valuable insights.
python machine-learning deep-learning transformer attention convolutional-neural-networks lime explainable-ai attention-maps shap permutation-importance visual-transformers
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Jun 6, 2024 - Jupyter Notebook
A take on highly imbalanced fraud classification using permutation importance to select top features and explaining the model using SHAP.
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Jun 18, 2019 - Jupyter Notebook
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.
principal-component-analysis lasso-regression feature-importance permutation-importance spearman-rank-correlation
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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.
python data-science machine-learning numpy scikit-learn pandas ensemble-model automl ensemble-machine-learning permutation-importance ice-plot
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Jun 15, 2024 - Jupyter Notebook
A take on highly imbalanced fraud classification using permutation importance to select top features and explaining the model using SHAP.
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Jun 18, 2019 - Jupyter Notebook
Comparing different tree-based algorithms to find the best model for cancelation prediction
machine-learning-algorithms eda prediction data-visualization classification hyperparameter-tuning boosting-tree bagging-trees permutation-importance
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May 12, 2020 - Jupyter Notebook
Contains analysis of Lyft ride attributes and how it affects demand surge in the city of Boston.
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Sep 20, 2021 - Jupyter Notebook
python machine-learning data-visualization feature-selection pca upsampling ensemble-model permutation-importance grid-search-algorithms data-scaling voting-ensemble c-index
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Mar 13, 2022 - Jupyter Notebook
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