In: Computer Science
Briefly describe why generalization bounds are important in Machine Learning
In supervised learning applications in machine learning and
statistical learning theory, generalization error (also known as
the out-of-sample error[1]) is a measure of how accurately an
algorithm is able to predict outcome values for previously unseen
data. Because learning algorithms are evaluated on finite samples,
the evaluation of a learning algorithm may be sensitive to sampling
error. As a result, measurements of prediction error on the current
data may not provide much information about predictive ability on
new data. Generalization error can be minimized by avoiding
overfitting in the learning algorithm. The performance of a machine
learning algorithm is measured by plots of the generalization error
values through the learning process, which are called learning
curves.