Projects in Awesome Lists tagged with partial-dependence-plot
A curated list of projects in awesome lists tagged with partial-dependence-plot .
https://github.com/koalaverse/vip
Variable Importance Plots (VIPs)
interaction-effect machine-learning partial-dependence-plot supervised-learning-algorithms variable-importance variable-importance-plots
Last synced: 04 Apr 2025
https://github.com/archd3sai/Customer-Survival-Analysis-and-Churn-Prediction
In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.
customer-churn-prediction customer-survival-analysis data-analysis explainable-ai flask-application hazard partial-dependence-plot random-forest shap-values survival-analysis
Last synced: 08 Apr 2025
https://github.com/bgreenwell/pdp
A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.
black-box-model machine-learning partial-dependence-function partial-dependence-plot r visualization
Last synced: 09 Apr 2025
https://github.com/bgreenwell/mlday18
Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18
decision-trees gradient-boosting-machine machine-learning partial-dependence-plot r random-forest variable-importance-plots
Last synced: 15 Apr 2025
https://github.com/bgreenwell/MLDay18
Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18
decision-trees gradient-boosting-machine machine-learning partial-dependence-plot r random-forest variable-importance-plots
Last synced: 26 Apr 2025
https://github.com/dlt3/odor-data-analysis
Complex odor analysis and interpretation
explainable-ai machine-learning partial-dependence-plot
Last synced: 25 Mar 2025
https://github.com/ksharma67/partial-dependent-plots-individual-conditional-expectation-plots-with-shap
The goal of SHAP is to explain the prediction of an instance x by computing the contribution of each feature to the prediction. The SHAP explanation method computes Shapley values from coalitional game theory. The feature values of a data instance act as players in a coalition.
eda individual-conditional-expectation matplotlib numpy pandas partial-dependence-plot python seaborn shap shapley-additive-explanations sklearn xgboost
Last synced: 13 May 2025
https://github.com/hbaniecki/robust-feature-effects
Robustness of Global Feature Effect Explanations (ECML PKDD 2024)
accumulated-local-effects dalex explainable-ai explainable-machine-learning explanatory-model-analysis feature-attribution iml interpretable-machine-learning partial-dependence-plot
Last synced: 25 Mar 2025
https://github.com/sambo-optimization/sambo
🎯📈 Sequantial and model-based optimization
bayesian-optimization bayesopt blackbox-optimization global-optimization global-optimization-algorithms hyperparameter-optimization hyperparameter-tuning machine-learning partial-dependence-plot sce-ua scientific-computing scikit-learn scikit-optimize scipy-optimize surrogate-based-optimization
Last synced: 17 Feb 2025
https://github.com/ksharma67/partial-dependent-plots-and-individual-conditional-expectation-plots
Individual Conditional Expectation (ICE) plots display one line per instance that shows how the instance's prediction changes when a feature changes. The Partial Dependence Plot (PDP) for the average effect of a feature is a global method because it does not focus on specific instances, but on an overall average.
eda gradient-boosting individual-conditional-expectation linear-regression matplotlib numpy pandas partial-dependence-plot python seaborn sklearn xgboost
Last synced: 13 May 2025