Celonis Product Documentation

Ask yourself the following questions:

Don't ask the questions the other way around!

If you understand the problem clearly, you should be able to list some potential solutions to test in order to generate the best model. Understand that you will likely have to try out a few solutions before you land on a good working model.

Exploratory data analysis can help you understand your data, but you can't yet claim that patterns you find generalize until you check those patterns against previously unseen data. Failure to check could lead you in the wrong direction or reinforce stereotypes or bias.

Data collected specifically for your task is going to be the most useful. In practice, you may not be able to do this, and you'll rely on whatever data you can get that's close enough. That's fine as long as you're aware of the cost, and as you can eventually get product logs, you can use those to build something more targeted to your task.

How much is "a lot?" That depends on the problem, but more data typically improves your model and therefore your model's predictive power. A good rule of thumb is to have at least thousands of examples for basic linear models, and hundreds of thousands for neural networks. If you have less data, consider a non-ML solution first.

Suppose you're trying to predict which horses will perform well in a race. You decide to tackle the problem with ML and use the horse’s eye color as a feature. You reason that eye color predicts which horses are prone to eye disease, which in turn could predict a horse’s speed and stamina. Maybe you’re wrong and you'll reject the hypothesis later based on evidence; that is, perhaps using eye color as a feature does not improve your model.

You should not try to make ML do the hard work of discovering which features are relevant for you. If you simply throw everything at the model and see what looks useful, your model will likely wind up overly complicated, expensive, and filled with unimportant features. In smaller datasets, you have a higher chance that a feature will be correlated with your label by chance within your sample of data. If you try lots of features without a hypothesis, you'll falsely believe these are relevant signals for your model. You wouldn't catch this until you tried to make predictions with your model and realized it did not generalize.

Aim to make decisions, not just predictions.

By decisions, we mean that your product should take action on the output of the model. ML is better at making decisions than giving you insights. If you have a bunch of data and want to find out "interesting" things about it, statistical approaches make more sense.

Make sure your predictions allow you to take a useful action. For example, a model that predicts the likelihood of clicking certain videos could allow a system to prefetch the videos most likely to be clicked.

Conversely, a model that predicts the probability that someone will click "thumbs down" for a specific YouTube video might be interesting, but we can't do anything useful with that knowledge.

Sometimes the prediction and decision are closely aligned, but in other cases, the relationship is less apparent. Review the table below for examples of some prediction/decision pairs.

Portions of this page are modifications based on work created and shared by Google and used according to terms described in the Creative Commons Attribution 4.0 License.