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Title: A Guide to Explainable AI Using Python - The Python Code

Open Graph Title: A Guide to Explainable AI Using Python - The Python Code

X Title: A Guide to Explainable AI Using Python - The Python Code

Description: An overview of model explainability and interpretability fundamentals, AI applications, and biases in AI model predictions. We looked at utilizing SHAP and LIME to explain a Logistic Regression model and how to explain and interpret an ensemble model.

Open Graph Description: An overview of model explainability and interpretability fundamentals, AI applications, and biases in AI model predictions. We looked at utilizing SHAP and LIME to explain a Logistic Regression model and how to explain and interpret an ensemble model.

X Description: An overview of model explainability and interpretability fundamentals, AI applications, and biases in AI model predictions. We looked at utilizing SHAP and LIME to explain a Logistic Regression model and how to explain and interpret an ensemble model.

Opengraph URL: https://thepythoncode.com/article/explainable-ai-model-python

X: @ThePythonCode1

direct link

Domain: www.thepythoncode.com

authorAdrien Payong
date2022-08-04T16:44:45.286000+00:00
categoryMachine Learning
og:typewebsite
og:imagehttps://thepythoncode.com/media/articles/explainable-ai-in-python.png
og:image:width600
og:image:height315
twitter:cardsummary_large_image
twitter:imagehttps://thepythoncode.com/media/articles/explainable-ai-in-python.png
twitter:image:altA Guide to Explainable AI Using Python

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Explainable Artificial Intelligence (https://en.wikipedia.org/wiki/Explainable_artificial_intelligence
XAI)https://en.wikipedia.org/wiki/Explainable_artificial_intelligence
machine learninghttps://www.thepythoncode.com/topic/machine-learning
herehttps://shap.readthedocs.io/en/latest/index.html
SHAP on GitHubhttps://github.com/slundberg/shap
herehttps://lime-ml.readthedocs.io/en/latest/
https://lime-ml.readthedocs.io/en/latest/
Researchgatehttps://www.researchgate.net/figure/LIME-explanation-for-hour-6-of-a-day-where-Actual077461539-Predicted073156154_fig2_344774233
ELI5https://eli5.readthedocs.io/en/latest/
Researchgatehttps://www.researchgate.net/figure/Feature-contribution-analysis-performed-by-ELI5-for-the-classification-of-different_fig5_349018095
Skaterhttps://oracle.github.io/Skater/
Kdnuggetshttps://www.kdnuggets.com/2018/12/explainable-ai-model-interpretation-strategies.html/2
semantic image segmentationhttps://www.thepythoncode.com/article/image-segmentation-using-huggingface-transformers-python
image captioninghttps://www.thepythoncode.com/article/image-captioning-with-pytorch-and-transformers-in-python
paper.https://mdpi-res.com/d_attachment/entropy/entropy-23-00018/article_deploy/entropy-23-00018-v2.pdf?version=1609160444
DepositPhotoshttps://depositphotos.com/
quantifyinghealthhttps://quantifyinghealth.com/interpret-logistic-regression-intercept/
https://drive.google.com/file/d/1d_K6CQ0tFMfhQNsyRWhUoLFJP3MD4jGU/view?usp=sharing
datasethttps://www.kaggle.com/code/arpitharavi/telecom-churn-notebook/data
paperhttps://arxiv.org/pdf/1602.04938.pdf
DLIMEhttps://arxiv.org/pdf/1906.10263.pdf
https://www.tutorialandexample.com/wp-content/uploads/2019/10/Decision-Trees-Root-Node.pnghttps://www.tutorialandexample.com/wp-content/uploads/2019/10/Decision-Trees-Root-Node.png
Iterative Dichotomiser 3 (ID3)https://en.wikipedia.org/wiki/ID3_algorithm
tutorialhttps://towardsdatascience.com/decision-trees-understanding-explainable-ai-620fc37e598d
websitehttps://towardsdatascience.com/linear-regression-on-boston-housing-dataset-f409b7e4a155
interpret libraryhttps://interpret.ml/docs/ebm.html
Morris sensitivity analysishttps://interpret.ml/docs/msa.html
Handling Imbalanced Datasets: A Case Study with Customer Churn.https://www.thepythoncode.com/article/handling-imbalanced-datasets-sklearn-in-python
https://drive.google.com/file/d/1d_K6CQ0tFMfhQNsyRWhUoLFJP3MD4jGU/view?usp=sharing
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https://www.thepythoncode.com/article/customer-churn-detection-using-sklearn-in-python
Customer Churn Prediction: A Complete Guide in Pythonhttps://www.thepythoncode.com/article/customer-churn-detection-using-sklearn-in-python
Learn how to perform data analysis and make predictive models to predict customer churn effectively in Python using sklearn, seaborn and more.https://www.thepythoncode.com/article/customer-churn-detection-using-sklearn-in-python
https://www.thepythoncode.com/article/dimensionality-reduction-feature-selection
Dimensionality Reduction Using Feature Selection in Pythonhttps://www.thepythoncode.com/article/dimensionality-reduction-feature-selection
Learn how to perform dimensionality reduction with feature selection such as recursively eliminating features, handling highly correlated features, and more using Scikit-learn in Python.https://www.thepythoncode.com/article/dimensionality-reduction-feature-selection
https://www.thepythoncode.com/article/kfold-cross-validation-using-sklearn-in-python
K-Fold Cross Validation using Scikit-Learn in Pythonhttps://www.thepythoncode.com/article/kfold-cross-validation-using-sklearn-in-python
Learn how you can perform K-Fold cross validation technique using the scikit-learn library in Python.https://www.thepythoncode.com/article/kfold-cross-validation-using-sklearn-in-python
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How to Extract Tables from PDF in Pythonhttps://www.thepythoncode.com/article/extract-pdf-tables-in-python-camelot
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How to Control your Mouse in Pythonhttps://www.thepythoncode.com/article/control-mouse-python
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