December 24, 2020
In machine learning ‘generalization performance’ is the measure of accuracy of predictions made by model on the test data i.e. unseen data. Other way to describe is it that, it is the measure of goodness of fit of the curve described by the machine learning model on test/new data.
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. The capability of the model to distinguish the general patterns in the training dataset from noise is termed as generalization performance.
One of the main reason behind doing the the test-train split is to measure the generalization performance of the model. If the accuracy metric, say r-squared statistics, is nearly same for both training and test data then the model has high generalization performance.
by : Monis Khan
Quick Summary:
In machine learning ‘generalization performance’ is the measure of accuracy of predictions made by model on the test data i.e. unseen data. Other way to describe is it that, it is the measure of goodness of fit of the curve described by the machine learning model on test/new data. When you feed data to a […]