Benchmarking simple machine learning models with feature extraction against modern black-box methods

And it appears they are not alone: Based on the 2017 Kaggle survey The State of ML and Data Science the most popular answer to the question "What data science methods are used at work?" was logistic regression with 63.5%. On the second place we find the decision tree with 49.9%. Those two methods constitute a class of very interpretable methods. However, to win a Kaggle competition one usually needs to employ a more modern, black-box like algorithm like boosted trees. In the Kaggle survey boosted trees ranked only 8th place with 23.9%.