Many ML projects, including Kaggle competitions, have a similar workflow. You start with a simple pipeline with a benchmark model. Next, you begin incorporating improvements: adding features, augmenting the data, tuning the model… On each iteration, you evaluate your solution and keep changes that improve the target metric. This workflow involves running a lot of […]
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