Hyperopt is a popular tool for optimizing hyperparameters in machine learning and data science.
Hyperopt enables you to perform optimization in parallel over a defined search space to find the optimal parameters. Continue reading to get started with Hyperopt.
Optimization with MATLAB and GAMS can aid decision-makers in the for-profit, non-profit, and public sectors.
Using optimization methods requires structuring problems, identifying objectives, and imposing constraints given a problem context. Continue reading to learn how to solve optmization problems in MATLAB and GAMS.
Probability distributions are used throughout quantitative fields where uncertainty or randomness are involved.
Probabilistic thinking requires knowledge of probability distributions and the relevant statistics associated with each. Understanding these distributions can help you identify opportunities to leverage one. Continue reading to learn about the core concepts and essential distributions with examples in Python.
SHAP or Shapley Additive Explanations helps you understand ML models.
The SHAP framework unifies the methods used to interpret and explain machine learning models. This post helps interpret and explain SHAP. Read this post to start getting into SHAP (with both high-level explanation and python example).
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I earned my Master's (M.S.) from Carnegie Mellon University and my Bachelor's (B.S.) from the Pennsylvania State University. All views on my website are my own and do not represent the organizations I work with unless clearly stated.
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