Taylor Scott Amarel

Experienced developer and technologist with over a decade of expertise in diverse technical roles. Skilled in data engineering, analytics, automation, data integration, and machine learning to drive innovative solutions.

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Category: Machine Learning Model Evaluation Guide

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Beyond Accuracy: A Practical Guide to Cross-Validation and Robust Model Performance Evaluation in Machine Learning

Introduction: The Pitfalls of Overfitting and the Need for Robust Evaluation In the relentless pursuit of building accurate and reliable machine learning models, data scientists often focus solely on achieving the highest possible accuracy score on a held-out test set. However, this singular focus can be misleading. A model that performs exceptionally well on one
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A Comprehensive Guide to Bayesian Inference for A/B Testing: Improve Decision-Making with Statistical Rigor

The Bayesian Revolution in A/B Testing: A New Era for Events and Entertainment In the high-stakes world of events and entertainment, where split-second decisions can make or break a campaign, traditional A/B testing methodologies are often found wanting. The frequentist approach, with its reliance on p-values and fixed sample sizes, struggles to provide the nuanced,
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