Overfitting, Cross-Validation Recommended reading: Neural nets: - PowerPoint PPT Presentation

Feb 05, 2024 •190 likes •439 views

Overfitting, Cross-Validation Recommended reading: Neural nets: Mitchell Chapter 4 Decision trees: Mitchell Chapter 3 Machine Learning 10-701 Tom M. Mitchell Carnegie Mellon University Overview Followup on neural networks

Overfitting, Cross-Validation Recommended reading: • Neural nets: Mitchell Chapter 4 • Decision trees: Mitchell Chapter 3 Machine Learning 10-701 Tom M. Mitchell Carnegie Mellon University
Overview • Followup on neural networks – Example: Face classification • Cross validation – Training error – Test error – True error • Decision trees – ID3, C4.5 – Trees and rules
# of gradient descent steps �
# of gradient descent steps �
# of gradient descent steps �
Cognitive Neuroscience Models Based on ANN’s [McClelland & Rogers, Nature 2003]
How should we choose the number of weight updates?
How should we choose the number of weight updates? How should we allocate N examples to training, validation sets? How will curves change if we double training set size? How will curves change if we double validation set size? What is our best unbiased estimate of true network error?
Overfitting and Cross Validation Overfitting: a learning algorithm overfits the training data if it outputs a hypothesis , h 2 H, when there exists h’ 2 H such that: where
Three types of error True error: Train set error: Test set error:
Bias in estimates Gives a biased (optimistically) estimate for Gives an unbiased estimate for
Leave one out cross validation Method for estimating true error of h’ • e=0 • For each training example z – Training on {data – z} – Test on single example z; if error, then e � e+1 Final error estimate (for training on sample of size |data|-1) is: e / |data|
Leave one out cross validation The leave-one-out error , e / |data|, gives an almost unbiased estimate for
Leave one out cross validation In fact, the e / |data| estimate of leave-one-out cross validation is a slightly pessimistic estimate of
How should we choose the number of weight updates? How should we allocate N examples to training, validation sets? How will curves change if we double training set size? How will curves change if we double validation set size? What is our best unbiased estimate of true network error?
What you should know: • Neural networks – Hidden layer representations • Cross validation – Training error, test error, true error – Cross validation as low-bias estimator

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