Machine Learning

December 24, 2020

How to measure generalization performance?

There are three methods that are used, at different stages of model life cycle, to measure the generalization performance of the model.

  1. Cross Validation: This is usually performed during data preprocessing and EDA phase.
  2. Test-Train split : Although this is one of the first steps in model creation, we use it to assess generalization performance during algorithm selection phase.
  3. Accuracy Monitoring: This is done after model deployment. It is used to check if the new data/real-time data is similar to the data on which we created the model. In other words, with this step we try to measure if the model generalizes to new data in a similar manner as it generalized to test data.

by : Monis Khan

Quick Summary:

There are three methods that are used, at different stages of model life cycle, to measure the generalization performance of the model. Cross Validation: This is usually performed during data preprocessing and EDA phase. Test-Train split : Although this is one of the first steps in model creation, we use it to assess generalization performance […]