model-interpretation
There are 1 repository under model-interpretation topic.
- Language:Jupyter Notebook 2852
PaddlePaddle / InterpretDL
InterpretDL: Interpretation of Deep Learning Models，基于『飞桨』的模型可解释性算法库。
Language:Python 231- chitra
aniketmaurya / chitra
A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.
Language:Python 224 - Language:Python 123
TannerGilbert / Model-Interpretation
Overview of different model interpretability libraries.
Language:Jupyter Notebook 38Hellisotherpeople / Active-Explainable-Classification
A set of tools for leveraging pre-trained embeddings, active learning and model explainability for effecient document classification
Language:HTML 29- Language:Jupyter Notebook 20
- Language:Python 12
akifcinar / Machine_Learning_Interpretability
Overview of machine learning interpretation techniques and their implementations
Language:Jupyter Notebook 4Craven-Biostat-Lab / mihifepe
Model Interpretability via Hierarchical Feature Perturbation
Language:Python 4SmellyArmure / OC_DS_Project7
Implémentation d'un modèle de scoring (OpenClassrooms | Data Scientist | Projet 7)
Language:Jupyter Notebook 4jagadishmali567 / BCG-Open-Access-Data-Science-Advanced-Analytics-Virtual-Experience-Program
The tasks I was required to complete as a part of the BCG Open-Access Data Science & Advanced Analytics Virtual Experience Program are all contained in this repository. 📊📈📉👨💻
business-understandingclient-communicationcommunicationcreativitydata-visualizationexploratory-data-analysishypothesis-framingjupyter-notebooksmachine-learning-algorithmsmachine-learning-engineeringmathematical-modellingmatplotlib-pyplotmodel-evaluationmodel-interpretationprogrammingpython-3seabornsynthesisfeature-engineeringstatistical-analysisLanguage:Jupyter Notebook 3bhattbhavesh91 / dtreeviz-tutorial
Visualize a Decision Tree using dtreeviz
Language:Jupyter Notebook 2GauravPandeyLab / ensemble_integration
Integrating multimodal data through heterogeneous ensembles
Language:Python 2mohitr7 / model-interpretability-for-machine-learning-models
Using LIME and SHAP for model interpretability of Machine Learning Black-box models.
Language:Jupyter Notebook 2This repository has all of the assignments I had to do for the Standard Bank Data Science Virtual Experience Program. 📉👨💻📊📈
Language:Jupyter Notebook 1EricPaul075 / OCP7-Dashboard-Backend-API
API backend to deploy a machine learning model to the web
Language:Python 0Streamlit dashboard frontend (user interface) to deploy a machine learning model to the web
Language:Python 0Harsh19012003 / Cognizant_AI_Internship
Exploratory data analysis, data modelling, model building and interpretation, machine learning production, quality assurance
Language:Jupyter Notebook 0KosiMbachuIgwe / Data-Science-and-Analytics-Virtual-Internship-BCG-Forage-
I performed 4 different tasks during the Data Science & Advanced Analytics virtual internship provided by BCG via Forage. I successfully utilized the Random Forest model to predict customers likely to churn, enabling me to proactively address potential losses.
Language:Jupyter Notebook 0MaggieUBC / Customer-Shopping-Behavior-Regression-Model
This project included a XGBoost Regression model, which predict the purchase possibility of a customer customer based on their online shopping behavior. In addtion, a recommendation model including both CF and CBF was built using customer purchase transaction data.
Language:Jupyter Notebook 0nidhimanthale / Statistical-Modelling
Statistical Modelling of Swine Flu Outbreak Data
logistic-regressionmodelinganalysisdataforward-selectioninteraction-testinghypothesis-testingmodel-fittinganova-testgoodness-of-fitpreliminary-analysisdiagnosticsoverdispersionsparse-dataoutlier-detectionbinomial-distributionlikelihood-ratio-testsignificance-testingmodel-buildingmodel-interpretationLanguage:R 0prajwalchapke055 / Cognizant-Artificial-Intelligence-Job-Simulation-Forage
Advise one of Cognizant’s clients on a supply chain issue by applying knowledge of machine learning models.
Language:Jupyter Notebook 0riccardoprosdocimi / forage-cognizant-ai
Cognizant Artificial Intelligence job simulation on Forage.
Language:Jupyter Notebook 0yakupkaplan / Sign-Language-Classification
Computer vision project for classifying American sign language.
Language:Python 0Lefteris-Souflas / Election-Classification-and-Clustering-Analysis
Creating predictive models to classify Trump's vote share and clustering counties based on demographics and economic variables. Report findings in PDF with detailed methodologies, model assessments, and R code for the project.
Language:RAnalyzed customer churn using transaction data. Built ML model to predict lapses. Dataset includes customer status, collection/redemption info, and program tenure. Delivered business presentation outlining modeling approach, findings, and churn reduction strategies.
rrambhia22 / Regression-Predictive-Modeling-and-Analysis-of-Bitcoin-Hashrate
The primary objective of this study is to develop a dependable and precise prediction model to forecast alterations in Bitcoin's hash rate.
arima-modelcorrelation-analysisdatapreprocessingdatavisualizationdecision-tree-regressionfeatureimportancesforecasting-modelslinear-regressionmachine-learning-algorithmsmodel-evaluation-metricsmodel-interpretationpca-analysispythonrandom-forest-regressionrecommendationsstatistical-analysistableau-desktoptimeseries-analysisdata-driven-reportssheelabhadra / TAMU-Datathon-Model-Interpretability
Materials for the TAMU Datathon 2020 workshop on "Model Interpretability".
Language:Jupyter NotebookSMRodrigo96 / Data-Science-Project-
Benchmarking bank data to enhance marketing strategies. Models: Decision Tree and Random Forest. Libraries: Pandas, Matplotlib, Seaborn, Scikit-Learn, Numpy. Findings: Customer patterns and seasonal behaviors.
Language:Jupyter Notebook
