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What is the best practice of applying Machine Learning model for me?

We recommend that you split your Machine Learning project into three phases and apply the following models:

  1. Early stage – Rapid prototyping: when you first start applying simpler Machine Learning techniques, decision trees and logistic regressions are the most appropriate options since they are very fast to train and easy to interpret. This is your opportunity to quickly see if the Machine Learning pipeline is going to work fine based on available data. You are basically looking to minimize the risk that a more complex algorithm may hide problems in your data that may later come back when you try to generalize your model.
  2. Middle stage – Proven application: after you learn that your data contains a good amount of signal, thus suitable to apply Machine Learning on, the next step is to get better performance with AI Studio AUTOML. With AI Studio AUTOML you can create highly performant models that can then be used to build a smart application supported by AI Studio’s machine learning automation capabilities.
  3. Late Stage – Critical performance: finally, when the Machine Learning problem is well understood, and it is worth spending the additional compute time to squeeze out the last few percentage points in model accuracy and evaluation performance, we advise you to use configure specific algoriths with different hyper parameters. This won’t make sense in every case due to the tradeoff between performance and complexity, but you can make this judgment for yourself based on the costs associated with incorrect predictions, e.g., showing the wrong ad to a user is not nearly as costly as diagnosing a patient incorrectly. For this purpose, you can use interactive operators of various class of algorithms
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