AlexIoannides / lime-interpretable-ml
An example of how the LIME algorithm can be used to provide real-world insight into the decision processes of a 'black-box' machine learning algorithm - in this case a Radom Forest regressor.
☆15Updated 6 years ago
Alternatives and similar repositories for lime-interpretable-ml:
Users that are interested in lime-interpretable-ml are comparing it to the libraries listed below
- Companion code for my PyData talk: "Introduction to Probabilistic Programming with PyMC3"☆13Updated 5 years ago
- H2OAI Driverless AI Code Samples and Tutorials☆37Updated 4 months ago
- Pre-Modelling Analysis of the data, by doing various exploratory data analysis and Statistical Test.☆51Updated last year
- Model explanation provides the ability to interpret the effect of the predictors on the composition of an individual score.☆13Updated 4 years ago
- Automatic Feature Engineering for Time Series☆17Updated 2 years ago
- ☆14Updated 5 years ago
- ☆14Updated 2 years ago
- Quick cheat sheet to time series models using NYC Taxi Data☆16Updated 5 years ago
- Recency, Frequency, and Monetary are three behavioral attributes and are quite simple, in that they can be easily computed for any databa…☆15Updated last year
- Materials for Machine Learning with H2O Open Platform at ODSC Masterclass Summit 2017☆12Updated 8 years ago
- Tutorials on session-based recommender systems☆11Updated 7 years ago
- Predict the poverty of households in Costa Rica using automated feature engineering.☆23Updated 4 years ago
- Repo for PyData 2019 Tutorial - New Trends in Estimation and Inference☆25Updated 5 years ago
- My work on UCSD CSE 250B Principles of Artificial Intelligence: Learning Algorithms☆13Updated 5 years ago
- Spark NLP for Streamlit☆15Updated 3 years ago
- How to do data science with Optimus, Spark and Python.☆19Updated 5 years ago
- Source code from my Master's thesis @Polytechnique Montréal. A solution to the assortment optimization problem, able to deal with large n…☆18Updated 7 years ago
- Short course on nonparametric inference in auditing and litigation, XXIX Foro Internacional de Estadistica, Puebla, MX☆15Updated 8 years ago
- A small wrapper to do Beta Boosting with XgBoost☆15Updated 3 years ago
- Demo on the capability of Yandex CatBoost gradient boosting classifier on a fictitious IBM HR dataset obtained from Kaggle. Data explorat…☆30Updated 5 years ago
- Code for blog posts.☆19Updated last year
- ☆20Updated 7 years ago
- Predicting the Likelihood to Purchase a Financial Product Following a Direct Marketing Campaign☆27Updated 2 years ago
- ☆19Updated 4 years ago
- Project template for highly effective data science workflows☆29Updated 11 months ago
- ☆21Updated last year
- ☆16Updated 7 years ago
- Material and slides for Boston NLP meetup May 23rd 2016☆17Updated 8 years ago
- OptimalFlow is an omni-ensemble and scalable automated machine learning Python toolkit, which uses Pipeline Cluster Traversal Experiments…☆27Updated last year
- Structural Time Series on US electricity demand data☆22Updated 4 years ago