December 24, 2020
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.
General patterns are those patterns which would still be present when new data is fed to the network. While patterns inherent to data points of the training dataset are classified as noise. Thus generalization of a model is characterized by it’s capability to identify the general patterns and ignore the noise.
by : Monis Khan
Quick Summary:
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. General patterns are those patterns which would still be present when […]