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GitHub topics: partial-dependence-plot

sambo-optimization/sambo

🎯📈 Sequantial and model-based optimization

Language: Python - Size: 108 KB - Last synced at: 23 days ago - Pushed at: about 2 months ago - Stars: 16 - Forks: 0

bgreenwell/pdp

A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.

Language: R - Size: 66.1 MB - Last synced at: 19 days ago - Pushed at: almost 3 years ago - Stars: 95 - Forks: 13

attilalr/pdp-tool

Partial dependence plot tool

Language: Jupyter Notebook - Size: 817 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 1 - Forks: 2

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.

Language: Jupyter Notebook - Size: 40.2 MB - Last synced at: 2 months ago - Pushed at: over 2 years ago - Stars: 197 - Forks: 72

koalaverse/vip

Variable Importance Plots (VIPs)

Language: R - Size: 407 MB - Last synced at: 15 days ago - Pushed at: over 1 year ago - Stars: 187 - Forks: 24

hbaniecki/robust-feature-effects

Robustness of Global Feature Effect Explanations (ECML PKDD 2024)

Language: Jupyter Notebook - Size: 1.15 MB - Last synced at: 3 months ago - Pushed at: 10 months ago - Stars: 1 - Forks: 0

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.

Language: Jupyter Notebook - Size: 1.07 MB - Last synced at: 26 days ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0

peijin0405/Machine-Learning-Analysis-of-International-Student-Mobility-A-Case-Study-of-China

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.

Language: HTML - Size: 5.25 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

nyuvis/partial_dependence

Python package to visualize and cluster partial dependence.

Language: Jupyter Notebook - Size: 166 MB - Last synced at: 10 months ago - Pushed at: about 3 years ago - Stars: 27 - Forks: 6

jianninapinto/Default-Loan-Predictor

Trained a classifier by using labeled data and SMOTE techniques to predict if a borrower will default on a loan. The machine learning model is intended to be used as a reference tool to help investors make informed decisions about lending to potential borrowers based on their ability to repay. The purpose is to lower risk and maximize profit.

Language: Jupyter Notebook - Size: 1.92 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

bgreenwell/MLDay18

Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18

Language: R - Size: 98.2 MB - Last synced at: about 1 month ago - Pushed at: over 6 years ago - Stars: 16 - Forks: 7

dlt3/Odor-data-analysis

Complex odor analysis and interpretation

Language: Jupyter Notebook - Size: 15.9 MB - Last synced at: 3 months ago - Pushed at: about 2 years ago - Stars: 2 - Forks: 0

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.

Language: Jupyter Notebook - Size: 554 KB - Last synced at: 26 days ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 1

nilsdenter/novelty_value_ml

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".

Language: Python - Size: 45.5 MB - Last synced at: over 2 years ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0

andrewlee977/lyft-demand-surge

Contains analysis of Lyft ride attributes and how it affects demand surge in the city of Boston.

Language: Jupyter Notebook - Size: 24.6 MB - Last synced at: 4 months ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

liyouzhang/Churn_Prediction

Predict churning or not from the real-world data of a ridesharing app

Language: Python - Size: 1.43 MB - Last synced at: about 2 years ago - Pushed at: almost 7 years ago - Stars: 2 - Forks: 0

soulbliss/Machine-learning-notebooks

Kaggle kernels and the respective implementations of ML procedures.

Language: Jupyter Notebook - Size: 53.7 KB - Last synced at: about 2 years ago - Pushed at: over 6 years ago - Stars: 1 - Forks: 0

mcarpanelli/Churn-Prediction-Rideshare

Data Science Case Study

Language: Python - Size: 1.42 MB - Last synced at: about 2 years ago - Pushed at: almost 7 years ago - Stars: 1 - Forks: 2

Related Keywords
partial-dependence-plot 18 machine-learning 10 random-forest 6 python 4 xgboost 4 scikit-learn 4 permutation-importance 3 logistic-regression 3 seaborn 3 explainable-ai 3 r 2 visualization 2 pandas 2 numpy 2 matplotlib 2 individual-conditional-expectation 2 eda 2 variable-importance-plots 2 sklearn 2 cost-benefit-analysis 2 prediction 2 linear-regression 2 ensemble-classifier 1 supervised-machine-learning 1 smote 1 shapley 1 roc-auc-curve 1 random-forest-classifier 1 push-pull 1 sql 1 interpretability 1 python-package 1 classification-report 1 confusion-matrix 1 one-hot-encoding 1 classification-algorithims 1 boosting-algorithms 1 pipeline 1 ml 1 cross-validation 1 python3 1 profit-curves 1 emsembling 1 adaboost 1 notebook 1 jupyter 1 heatmap 1 patentsview 1 patents 1 machinelearning-python 1 machinelearning 1 machine-learning-models 1 machine-learning-interpretation 1 machine-learning-interpretability 1 machine-learning-algorithms 1 kpss 1 gradient-boosting 1 gradient-boosting-machine 1 decision-trees 1 naive-bayes-classifier 1 hazard 1 flask-application 1 data-analysis 1 customer-survival-analysis 1 customer-churn-prediction 1 exploratory-data-analysis 1 partial-dependence-function 1 black-box-model 1 surrogate-based-optimization 1 scipy-optimize 1 scikit-optimize 1 scientific-computing 1 sce-ua 1 hyperparameter-tuning 1 hyperparameter-optimization 1 global-optimization-algorithms 1 global-optimization 1 blackbox-optimization 1 bayesopt 1 bayesian-optimization 1 kneighborsclassifier 1 ice-plot 1 decision-tree-classifier 1 data-visualization 1 data-science 1 auc-roc-curve 1 shapley-additive-explanations 1 shap 1 interpretable-machine-learning 1 iml 1 feature-attribution 1 explanatory-model-analysis 1 explainable-machine-learning 1 dalex 1 accumulated-local-effects 1 variable-importance 1 supervised-learning-algorithms 1 interaction-effect 1 survival-analysis 1 shap-values 1