Is testing a ML model against data that could have been previously used in model training a proper evaluation phase in machine learning?
Tuesday, 14 November 2023
by Hema Gunasekaran
The evaluation phase in machine learning is a critical step that involves testing the model against data to assess its performance and effectiveness. When evaluating a model, it is generally recommended to use data that has not been seen by the model during the training phase. This helps to ensure unbiased and reliable evaluation results.
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
Tagged under:
Artificial Intelligence, Evaluation Phase, Machine Learning, Model Training, Overfitting, Validation Set