Engineering data versus model variety

Having a large choice of algorithms for your predictions is always a good thing, but at the end of the day, domain knowledge and the ability to extract meaningful features from clean data is often what wins the game.

Kaggle is a well-known platform for predictive analytics competitions, where the best data scientists across the world compete to make predictions on complex datasets. In these predictive competitions, gaining a few decimals on your prediction score is what makes the difference between earning the prize or being just an extra line on the public leaderboard among thousands of other competitors. One thing Kagglers quickly learn is that choosing and tuning the model is only half the battle. Feature extraction or how to extract relevant predictors from the dataset is often the key to winning the competition.

In real life, when working on business related problems, the quality of the data processing phase and the ability to extract meaningful signal out of raw data is the most important and time consuming part of building an efficient predictive model. It is well know that "data preparation accounts for about 80% of the work of data scientists" (http://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/). Model selection and algorithm optimization remains an important part of the work but is often not the deciding factor when implementation is concerned.

A solid and robust implementation that is easy to maintain and connects to your ecosystem seamlessly is often preferred to an overly complex model developed and coded in-house, especially when the scripted model only produces small gains when compared to a service based implementation.