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As a non ML expert, I never quite understand where you draw the line between feature engineering and the architecture engineering required for Deep Learning to
by anonymousDan 5y ago
As a non ML expert, I never quite understand where you draw the line between feature engineering and the architecture engineering required for Deep Learning to work. Is it just a spectrum or is there some fundamental difference?
- ford 5y agoI'm assuming by architecture engineering you mean the process of choosing the model + hyperparameters that perform best. I think of feature vs architecture engineering as two not-quite perpendicular axes that you can do work on. Feature engineering involves selecting & transforming inputs such that the model has access to the most meaningful/easiest to interpret data. Architecture engineering involves choosing the appropriate model architecture. Suppose we were building a model to predict a business's sales for the next 12 months based on the prior 12 months. An important input is the sales for the prior year. A feature engineering task would be choosing to take the sum, median, or average sales per day based on what you know about the distribution. Architecture engineering would involve trying a few different models & tuning their hyperparameters to be most accurate. The two areas are related - because you may know that the business's sales on one day are highly dependent on the previous few days. Knowing this, you may instead choose features + an architecture that instead of predicting sales for an entire 12 months period, predicts sales on a day-by-day basis (which can then be summed).