10-overfitting - Inductive Learning Setting Foundations of...

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1 Foundations of Artificial Intelligence Statistical Learning and Overfitting CS472 – Fall 2007 Thorsten Joachims Inductive Learning Setting Learning as Prediction: • Learner induces a general rule h from a set of observed examples that classifies new examples accurately. New examples h: X Æ Y Testing Machine Learning Algorithms Machine Learning Experiment: – Gather training examples D train – Run learning algorithm on D train to produce h – Gather Test Examples D test – Apply h to D test and measure how many test examples are predicted correctly by h Real-world Process (x 1 ,y 1 ), …, (x n ,y n ) Learner (x n+1 ,y n+1 ), … Training Data D train Test Data D test drawn randomly drawn randomly h D train Test/Training Split Real-world Process (x 1 ,y 1 ), …, (x n ,y n ) Learner (x n+1 ,y n+1 ), … Training Data D train Test Data D test drawn randomly drawn randomly h D train Real-world Process (x 1 ,y 1 ), …, (x n ,y n ) Learner (x 1 ,y 1 ),…(x k ,y k ) Training Data D train Test Data D test split randomly split randomly h D train Data D drawn randomly Measuring Prediction Performance Performance Measures Error Rate – Fraction (or percentage) of false predictions Accuracy – Fraction (or percentage) of correct predictions
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10-overfitting - Inductive Learning Setting Foundations of...

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