<ul><li>Building benchmarks for models is crucial in data science projects to evaluate performance accurately.</li><li>A benchmark consists of standardized metrics and simple baseline models for comparison.</li><li>By comparing models to benchmarks, improvements can be tracked reliably and performance assessed.</li><li>Creating benchmarks helps in setting clear objectives, enhancing client communication, and aiding in model selection.</li><li>Benchmarks also assist in detecting model drift, ensuring consistency across datasets, and providing immediate feedback.</li><li>Defining metrics, setting benchmarks, and running comparisons are key steps in building a benchmark for models.</li><li>Business-driven metrics like financial gain can be crucial in evaluating model performance for specific use cases.</li><li>The article provides examples of simple benchmarks like random model, majority model, simple XGB, and simple KNN.</li><li>Custom benchmarks tailored to specific business needs, such as customer churn scenarios, can also be designed.</li><li>While benchmarks are valuable, potential drawbacks include non-informative benchmarks, misinterpretation by stakeholders, and overfitting.</li></ul>

How To Build a Benchmark for Your Models

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