15 years of software architect experience summary: In the ML field, 5 pits that beginners have stepped on

Recently, software architect, data scientist, and Kaggle guru Agnis Liukis wrote an article in which he talks about the most common types of machine learningSolutions to some beginner mistakes to make sure beginners understand and avoid them.

1. Data normalization is not used where needed

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2, think the more features the better

3. Use a tree-based model where extrapolation is required

4. Use data normalization where it is not neededp>

5. Leaking information between train and validation/test sets

Original link:

https://towardsdatascience.com/5-typical-beginner-mistakes-in-machine-learning-3544bd4109b