Using inductive bias as a guide for effective machine learning prototyping

What makes working on new machine learning (ML) use cases so exciting, and at times so frustrating, is ML’s lack of hard and fast rules. A few aspects of the model development process can be codified; for example, data should always be separated into strictly disjoint training and test sets to ensure that model performance isn’t attributable to overfitting. But…




Thoughts from the Engineering team at Flatiron Health. We're building technology that enables cancer researchers and care providers to learn from the experience of every patient.

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Alexander Rich

Alexander Rich

Data Insights Engineer at Flatiron Health and cognitive psychology PhD

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