Topic: "partial-dependence-plots"
ptogias/rf-lipidomics
Addressing n<<p biology problems with interpretable random forest results via partial dependence plots. - Construction of an ideal high-density lipoprotein
Size: 2.74 MB - Last synced at: almost 2 years ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 0
hectoramirez/CS_salary_vs_education
Partial-dependence plots on supervised-learning models
Language: Jupyter Notebook - Size: 1.03 MB - Last synced at: over 2 years ago - Pushed at: about 5 years ago - Stars: 1 - Forks: 0
KUSHALKUMARD/Customer-Churn-Prediction-App-Project
In this project, I utilized survival analysis models to assess how the likelihood of customer churn changes over time and to calculate customer Lifetime Value (LTV). Additionally, I implemented a Random Forest model to predict customer churn and deployed this model using a Flask web application.
Language: Jupyter Notebook - Size: 24.5 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0
gerdis/ordinalForest_demo
little demo project on how to evaluate and interprete ordinal forests
Language: R - Size: 14.7 MB - Last synced at: 4 months ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0
EdoardoGruppi/IoT_Assignment
This project aims to analyze and model the information retrieved by a real-world IoT system in order to understand which are the relationships in the dataset chosen between the dependent variables and the selected target feature. To help the understanding of the inner working of the models adopted, several machine learning interpretability techniques are exploited.
Language: Jupyter Notebook - Size: 684 KB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0
