2.3 Over-fitting In statistics and machine learning,overfitting occurs when a statistical model describes random error or noise instead of the underlying relationship. Overfitting generally occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. A model that has been over-fit will generally have poor predictive performance,as it can exaggerate minor fluctuations in the data. The possibility of overfitting exists because the criterion used for training the model is not the same as the criterion used to judge the efficacy of a model
2.3 Over-fitting • In statistics and machine learning, overfitting occurs when a statistical model describes random error or noise instead of the underlying relationship. • Overfitting generally occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. • A model that has been over-fit will generally have poor predictive performance, as it can exaggerate minor fluctuations in the data. • The possibility of overfitting exists because the criterion used for training the model is not the same as the criterion used to judge the efficacy of a model
2.3 Over-fitting In the curve fitting example,when M+1 N,the model has too many parameters and thus become so complicated that extreme over- fitting occurs. In order to avoid overfitting,it is necessary to use additional techniques (e.g.cross-validation,regularization,early stopping,pruning,Bayesian priors on parameters or model comparison)
2.3 Over-fitting In the curve fitting example, when , the model has too many parameters and thus become so complicated that extreme overfitting occurs. In order to avoid overfitting, it is necessary to use additional techniques (e.g. cross-validation, regularization, early stopping, pruning, Bayesian priors on parameters or model comparison)
2.3 Over-fitting With all the information above,there is still one question: We know that a power series expansion of the function sin(2nx) contains terms of all orders,so we might expect that results should improve monotonically as we increase M. The graphs below should give some insight. Data Set Size:N=15 Data Set Size:N=100 9th Order Polynomial 9th Order Polynomial 0 N=15 o8。 88 N=100 o8的0 0 0 % 00 0 0 0
2.3 Over-fitting With all the information above, there is still one question: We know that a power series expansion of the function contains terms of all orders, so we might expect that results should improve monotonically as we increase M. The graphs below should give some insight