Everyone talks about how machine learning will transform business forever and generate massive outcomes. However, it’s surprisingly simple to draw completely wrong conclusions from statistical models, and “correlation does not imply causation” is just the tip of the iceberg.
The trend of the democratization of data science further increases the risk for applying models in a wrong way. In this webinar, Founder and President Dr. Ingo Mierswa discusses:
How highly-correlated features can overshadow the patterns your machine learning model is supposed to find – this leads to models which will perform worse in production than during model building
How incorrect cross-validation leads to over-optimistic estimations of your model accuracy, and especially the impact of data pre-processing on the accuracy of machine learning models
How feature engineering can lift simple models like linear regression to the accuracy of deep learning – but comes with the advantages of understandability