Displaying 1 to 6 from 6 results

diabetes_use_case - Sample use case for Xavier AI in Healthcare conference: https://www

  •    Jupyter

Recent advances enable practitioners to break open machine learning’s “black box”. From machine learning algorithms guiding analytical tests in drug manufacture, to predictive models recommending courses of treatment, to sophisticated software that can read images better than doctors, machine learning has promised a new world of healthcare where algorithms can assist, or even outperform, professionals in consistency and accuracy, saving money and avoiding potentially life-threatening mistakes. But what if your doctor told you that you were sick but could not tell you why? Imagine a hospital that hospitalized and discharged patients but was unable to provide specific justification for these decisions. For decades, this was a roadblock for the adoption of machine learning algorithms in healthcare: they could make data-driven decisions that helped practitioners, payers, and patients, but they couldn’t tell users why those decisions were made.

interpretable_machine_learning_with_python - Practical techniques for interpreting machine learning models

  •    Jupyter

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. In this notebook, I will demonstrate how to use monotonicity constraints in the popular open source gradient boosting package XGBoost to train a simple, accurate, nonlinear classifier on the UCI credit card default data. Once we have trained a monotonic XGBoost model, we will use partial dependence plots and individual conditional expectation (ICE) plots to investigate the internal mechanisms of the model and to verify its monotonic behavior. 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. ICE plots can be used to create more localized descriptions of model predictions, and ICE plots pair nicely with partial dependence plots. An example of generating regulator mandated reason codes from high fidelity Shapley explanations for any model prediction is also presented. The combination of monotonic XGBoost, partial dependence, ICE, and Shapley explanations is likely the most direct way to create an interpretable machine learning model today.

Benchmarking-and-MLI-experiments-on-the-Adult-dataset - Contains benchmarking and interpretability experiments on the Adult dataset using several libraries

  •    Jupyter

The initial experiments were a part of an assignment given from TCS ILP Innovations' Lab. Later as my appetite for the wonderful field of machine learning increased, I decided to give it another try and try out the new libraries. It includes benchmarking and interpretability experiments on the Adult Data set using libraries like fastai, h2o and interpret. Along with these, I have shown how one can use the interpret library to construct explanations for sklearn models. Note that keras models can be converted to sklearn variants and this enables interpret to work equally on these models as well.

ml-fairness-framework - FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

  •    Jupyter

Animated Investment Management Research at Sov.ai — Sponsoring open source AI, Machine learning, and Data Science initiatives. This is a holistic approach to implement fair outputs at the individual and group level. Some of the methods developed or used includes quantitative monotonic measures, residual explanations, benchmark competition, adverserial attacks, disparate error analysis, model agnostic pre-and post-processing, reasoning codes, counterfactuals, contrastive explanations, and prototypical examples.

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