•Given:
–Training set of N
examples
–A class of learning models (e.g.
decision trees, neural networks, …)
•Method:
–Train multiple (k) models on
different samples (data splits) and average their
predictions
–Predict (test) by averaging the
results of k models
•Goal:
–Improve the accuracy of one model by using its multiple copies
–Average of misclassification
errors on different data splits gives a better estimate of
the predictive ability of a learning method