https://github.com/csinva/hierarchical_dnn_interpretationshttps://www.h2o.ai/wp-content/uploads/2019/08/An-Introduction-to-Machine-Learning-Interpretability-Second-Edition.pdfdownload the GitHub extension for Visual StudioThe Book of Why: The New Science of Cause and Effecthttps://github.com/jphall663/xai_misconceptionshttps://journal.r-project.org/archive/2017/RJ-2017-016/RJ-2017-016.pdfhttps://github.com/ehrlinger/ggRandomForestshttps://channel9.msdn.com/Events/useR-international-R-User-conferences/useR-International-R-User-2017-Conference/Show-Me-Your-Model-tools-for-visualisation-of-statistical-modelshttps://www.youtube.com/watch?v=B3PtcF-6Dtchttps://papers.nips.cc/paper/2728-result-analysis-of-the-nips-2003-feature-selection-challenge.pdfhttp://www.aies-conference.com/accepted-papers/https://zoom.us/recording/play/0y-iI9HamgyDzzP2k_jiTu6jB7JgVVXnjWZKDMbnyRTn3FsxTDZy6Wkrj3_ekx4Jhttps://cran.r-project.org/web/packages/ggfortify/index.htmlhttps://dx.doi.org/10.1007/s10994-006-6226-1https://blackboxnlp.github.io/program.htmlhttp://www.cs.man.ac.uk/~gbrown/publications/pocockPhDthesis.pdfhttps://speakerdeck.com/sritchie/just-so-stories-for-ai-explaining-black-box-predictionshttps://github.com/taolei87/rcnn/tree/master/code/rationaleYou signed in with another tab or window. Decision tree surrogates, reason codes, and ensembles of explanationsExamples of techniques for training interpretable machine learning (ML) models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.Sensitivity analysis is the perturbation of data under a trained model. A basic DIA procedure is then conducted using the information stored in the confusion matrices and some traditional fair lending measures.Gradient boosting machines (GBMs) and other complex machine learning models are popular and accurate prediction tools, but they can be difficult to interpret. The higher the interpretability of a machine learning model, the easier it is for someone to comprehend why certain decisions or predictions have been made. To do so:Once local samples have been generated, we will fit LIME models to understand local trends in the complex model’s predictions. We will also analyze the global variable importance of the GBM and compare this information to the surrogate model, our domain expertise, and our reasonable expectations.Anaconda Python, Java, Git, and GraphViz must be added to your system path.Fairness is an incredibly important, but highly complex entity. As the programmer of an algorithm you want to know whether you can trust the learned model. Use Git or checkout with SVN using the web URL. ICE plots can be used to create more localized descriptions of model predictions, and ICE plots pair nicely with partial dependence plots. LOCO enables us to calculate the local contribution each input variable makes toward each model prediction. The higher the interpretability of a machine learning model, the easier it is for someone to comprehend why certain decisions or predictions have been made. We will then rank the local contributions to generate reason codes that describe, in plain English, the model’s decision process for every prediction.Start the docker image and the Jupyter notebook server.interpretable_machine_learning_with_pythonCheck the registered email inbox and use the temporary password to login to Aquarium.We’ll further enhance trust in our model using residual analysis.
Machine learning is being built into many products and processes of our daily lives, yet decisions made by machines don't automatically come with an explanation. This procedure is often known as disparate impact analysis (DIA). The later chapters focus on analyzing complex models and their decisions.
DIA is far from perfect, as it relies heavily on user-defined thresholds and reference levels to measure disparity and does not attempt to remediate disparity or provide information on sources of disparity, but it is a fairly straightforward method to quantify your model’s behavior across sensitive demographic segments or other potentially interesting groups of observations. However, there is a practical way to discuss and handle observational fairness, or how your model predictions affect different groups of people. In this notebook, we will create residual plots for a complex model to debug any accuracy problems arising from overfitting or outliers.Install the correct packages for the example notebooks.Monotonicity constraints can turn opaque, complex models into transparent, and potentially regulator-approved models, by ensuring predictions only increase or only decrease for any change in a given input variable. Then we’ll train a decision tree surrogate model on the original inputs and predictions of the complex GBM model and see how the variable importance and interactions displayed in the surrogate model yield an overall, approximate flowchart of the complex model’s predictions. - jphall663/interpretable_machine_learning_with_python An example of generating regulator mandated reason codes from high fidelity Shapley explanations for any model prediction is also presented. If it’s good enough for multibillion-dollar credit portfolios, it’s probably good enough for your project. I am passionate about using statistics and machine learning on data to make humans and machines smarter. Residuals refer to the difference between the recorded value of a target variable and the predicted value of a target variable for each row in a data set. No description or website provided.
Awesome Interpretable Machine Learning . Partial dependence plots show us the way machine-learned response functions change based on the values of one or two input variables of interest while averaging out the effects of all other input variables.
This branch is 32 commits behind lopusz:master. This notebook focuses on using these different types of sensitivity analysis to discover error mechanisms and security vulnerabilities and to assess stability and fairness in a trained XGBoost model. An explanation increases the trust in the decision and in the machine learning model. The combination of monotonic XGBoost, partial dependence, ICE, and Shapley explanations is likely one of the most direct ways to create an interpretable machine learning model today.To get a better picture of the complex model’s local behavior and to enhance the accountability of the model’s predictions, we will use a variant of the leave-one-covariate-out (LOCO) technique.