partial-dependence-plot

There are 1 repository 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.
customer-survival-analysis random-forest explainable-ai data-analysis flask-application survival-analysis shap-values partial-dependence-plot hazard customer-churn-prediction
Language:Jupyter Notebook 232
koalaverse / vip
Variable Importance Plots (VIPs)
variable-importance interaction-effect partial-dependence-plot supervised-learning-algorithms machine-learning variable-importance-plots
Language:R 189
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
Language:R 98
nyuvis / partial_dependence
Python package to visualize and cluster partial dependence.
interpretability machine-learning partial-dependence-plot python-package visualization
Language:Jupyter Notebook 28
sambo
sambo-optimization / sambo
🎯 📈 Sequential And Model-Based Optimization with SCE-UA, SMBO, and SHGO algos. No deps—SOTA perfomance.
bayesian-optimization blackbox-optimization global-optimization global-optimization-algorithms sce-ua scikit-learn scikit-optimize scipy-optimize surrogate-based-optimization bayesopt hyperparameter-optimization hyperparameter-tuning machine-learning partial-dependence-plot scientific-computing hyperparameter-search optimize optimization-tools hydrological-modelling optimization-algorithms
Language:Python 24
bgreenwell / MLDay18
Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18
machine-learning r random-forest gradient-boosting-machine decision-trees partial-dependence-plot variable-importance-plots
Language:R 15
Maggie0927 / AirlinePassengerSatisfaction
Data Mining Final Project
clustering customer-segmentation ensemble-model exploratory-data-analysis feature-engineering light-gbm machine-learning partial-dependence-plot xgboost
Language:Jupyter Notebook 5
liyouzhang / Churn_Prediction
Predict churning or not from the real-world data of a ridesharing app
adaboost cost-benefit-analysis emsembling logistic-regression machine-learning partial-dependence-plot prediction profit-curves python3 random-forest
Language:Python 3
akshatsinghal92 / Product-recommendation-analysis
Predicting product recommendation score using the data available on the website of the client
web-extraction selenium-python nlp python seaborn-plots word-embeddings regression-models machine-learning textblob-sentiment-analysis universal-sentence-encoder partial-dependence-plot
Language:Jupyter Notebook 2
dlt3 / Odor-data-analysis
Complex odor analysis and interpretation
explainable-ai machine-learning partial-dependence-plot
Language:Jupyter Notebook 2
hbaniecki / robust-feature-effects
Robustness of Global Feature Effect Explanations (ECML PKDD 2024)
dalex explainable-ai explainable-machine-learning explanatory-model-analysis feature-attribution iml interpretable-machine-learning partial-dependence-plot accumulated-local-effects
Language:Jupyter Notebook 2
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 sklearn xgboost shapley-additive-explanations
Language:Jupyter Notebook 2
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.
auc-roc-curve data-science data-visualization decision-tree-classifier ice-plot kneighborsclassifier linear-regression machine-learning naive-bayes-classifier partial-dependence-plot permutation-importance push-pull random-forest sql
Language:HTML 2
andrewlee977 / lyft-demand-surge
Contains analysis of Lyft ride attributes and how it affects demand surge in the city of Boston.
heatmap jupyter notebook partial-dependence-plot permutation-importance scikit-learn
Language:Jupyter Notebook 1
attilalr / pdp-tool
Partial dependence plot tool
exploratory-data-analysis partial-dependence-plot
Language:Jupyter Notebook 1
mcarpanelli / Churn-Prediction-Rideshare
Data Science Case Study
boosting-algorithms classification-algorithims cost-benefit-analysis ensemble-classifier logistic-regression machine-learning partial-dependence-plot prediction python random-forest
Language:Python 1
mscsep / TRACE
Meta-analysis of learning and memory in PTSD
meta-analysis metacart metafor metaforest partial-dependence-plot random-effects-model random-forest systematic-review
Language:R 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".
patents kpss scikit-learn machine-learning permutation-importance partial-dependence-plot machine-learning-interpretation patentsview machine-learning-algorithms machine-learning-interpretability machine-learning-models machinelearning machinelearning-python
Language:Python 1
soulbliss / Machine-learning-notebooks
Kaggle kernels and the respective implementations of ML procedures.
xgboost random-forest ml pipeline cross-validation partial-dependence-plot seaborn
Language:Jupyter Notebook 1
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.
classification-report confusion-matrix logistic-regression one-hot-encoding partial-dependence-plot python random-forest-classifier roc-auc-curve scikit-learn shapley smote supervised-machine-learning xgboost
Language:Jupyter Notebook 0
mscsep / OIC_meta
Meta-analysis of the rodent object-in-context task
random-forest meta-analysis partial-dependence-plot random-effects-model systematic-review metafor metaforest object-in-context-task
Language:R 0
AnantaOjha / 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
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 pandas partial-dependence-plot python xgboost matplotlib numpy seaborn sklearn
Language:Jupyter Notebook

partial-dependence-plot

archd3sai / Customer-Survival-Analysis-and-Churn-Prediction

koalaverse / vip

bgreenwell / pdp

nyuvis / partial_dependence

sambo-optimization / sambo

bgreenwell / MLDay18

Maggie0927 / AirlinePassengerSatisfaction

liyouzhang / Churn_Prediction

akshatsinghal92 / Product-recommendation-analysis

dlt3 / Odor-data-analysis

hbaniecki / robust-feature-effects

ksharma67 / Partial-Dependent-Plots-Individual-Conditional-Expectation-Plots-With-SHAP

peijin0405 / Machine-Learning-Analysis-of-International-Student-Mobility

andrewlee977 / lyft-demand-surge

attilalr / pdp-tool

mcarpanelli / Churn-Prediction-Rideshare

mscsep / TRACE

nilsdenter / novelty_value_ml

soulbliss / Machine-learning-notebooks

jianninapinto / Loan-Default-Risk-Prediction

mscsep / OIC_meta

AnantaOjha / Customer-Survival-Analysis-and-Churn-Prediction

ksharma67 / Partial-Dependent-Plots-and-Individual-Conditional-Expectation-Plots