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
