December 24, 2020
In machine learning ‘Generalization Failure’ is synonymous with model overfitting. When you feed data to a machine learning model, it learns the underlying patterns that describe the relationship among the data points of the given dataset. Some of these are general patterns, while other are inherent to the data points of the training dataset. When the model distinguish the general patterns in the training dataset from noise, then it is said to be suffering from generalization failure.
One of the main reason behind doing the the test-train split is to identify if the model is suffering from generalization failure. When a model performs exceedingly well of training data in comparison to test data, it is a clear sign that the model has failed to generalize. In other words generalization failure occurs when the model ails from high variance.
by : Monis Khan
Quick Summary:
In machine learning ‘Generalization Failure’ is synonymous with model overfitting. When you feed data to a machine learning model, it learns the underlying patterns that describe the relationship among the data points of the given dataset. Some of these are general patterns, while other are inherent to the data points of the training dataset. When […]