partial-dependence-plot
There are 22 repositories under partial-dependence-plot topic.
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.
koalaverse/vip
Variable Importance Plots (VIPs)
bgreenwell/pdp
A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.
nyuvis/partial_dependence
Python package to visualize and cluster partial dependence.
bgreenwell/MLDay18
Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18
sambo-optimization/sambo
🎯📈 Sequantial and model-based optimization
Maggie0927/AirlinePassengerSatisfaction
Data Mining Final Project
liyouzhang/Churn_Prediction
Predict churning or not from the real-world data of a ridesharing app
akshatsinghal92/Product-recommendation-analysis
Predicting product recommendation score using the data available on the website of the client
dlt3/Odor-data-analysis
Complex odor analysis and interpretation
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.
peijin0405/Machine-Learning-Analysis-of-International-Student-Mobility
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.
attilalr/pdp-tool
Partial dependence plot tool
hbaniecki/robust-feature-effects
Robustness of Global Feature Effect Explanations (ECML PKDD 2024)
mcarpanelli/Churn-Prediction-Rideshare
Data Science Case Study
mscsep/TRACE
Meta-analysis of learning and memory in PTSD
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".
soulbliss/Machine-learning-notebooks
Kaggle kernels and the respective implementations of ML procedures.
jianninapinto/Loan-Default-Risk-Prediction
Trained a classifier by using labeled data and oversampling and undersampling techniques to predict if a borrower will default on a loan. The 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 & maximize profit.
mscsep/OIC_meta
Meta-analysis of the rodent object-in-context task
andrewlee977/lyft-demand-surge
Contains analysis of Lyft ride attributes and how it affects demand surge in the city of Boston.
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.