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Overfitting and underfitting

Another way of stating the second detriment is that an overfit model is much more sensitive to any condition that deviates from the conditions used to collect the calibration samples. [Pg.407]

A less tempting alternative, which is equally dangerous, is to underfit a model. In this case, the model is not sufficiently complex to account for interfering effects in the analyzer data. As a result, the model can provide inaccurate results even in cases where it is applied to conditions that were used to build it  [Pg.408]

In PAT, one is often faced with the task of building, optimizing, evaluating, and deploying a model based on a limited set of calibration data. In such a situation, one can use model validation and cross-validation techniques to perform two of these functions namely to optimize the model by determining the optimal model complexity and to perform preliminary evaluation of the model s performance before it is deployed. There are several validation methods that are commonly used in PAT applications, and some of these are discussed below. [Pg.408]


Akaike s criterion and its derivations has been called by some [see Verbeke and Molenberghs (2000) for example] as a minimization function plus a penalty term for the number of parameters being estimated. As more model parameters are added to a model, 2LL tends to decrease but 2 p increases. Hence, AIC may decrease to a point as more parameters are added to a model but eventually the penalty term dominates the equation and AIC begins to increase. Conceptually this fits into the concept of bias-variance trade-off or the trade-off between overfitting and underfitting. [Pg.25]

Model development in drug development is usually empirical or exploratory in nature. Models are developed using experimental data and then refined until a reasonable balance is obtained between overfitting and underfitting. This iterative process in model selection results in models that have overly optimistic inferential properties because the uncertainty in the model is not taken into account. No universally accepted solution to this problem has been found. [Pg.56]

Theory of Overfitting and Underfitting Control, RM and SRM Principles of Statistical Learning Theory... [Pg.12]

For k 1 (1 -NN), a new object would always get the same class membership as its next neighbor. Thus, for small values of k, it is easily possible that classes do no longer form connected regions in the data space, but they can consist of isolated clouds. The classification of new objects can thus be poor if k is chosen too small or too large. In the former case, we are concerned with overfitting, and in the latter case with underfitting. [Pg.229]

Overfitting is the commonest problem in multivariate statistical procedures when the number of variables is greater than objects (samples) one can fit an elephant with enough variables. Tabachnick and Fidell (1983) have suggested minimum requirements for some multivariate procedures to avoid the overfitting or underfitting that can occur in a somewhat unpredictable manner, regardless of the multivariate procedure chosen. [Pg.159]

Validation is often also used in order to find the optimal dimensionality of a multivariate model, that is, to avoid either overfitting or underfitting or incorrect interpretation [25]. This is not restricted to regression but is also important for exploratory analysis and methods such as PCA [26]. [Pg.160]

Figure 4.14 Taguchi s loss function to show the trade-off between bias (underfitting) and variance (overfitting) see Ref. [30] for more mathematical details. Here, k would be the optimum dimensionality of the PLS model. [Pg.204]

As the number of parameters in a model increases, the closeness of the predicted values to the observed values increases, but at the expense of estimating the model parameters. In other words, the residual sum of squares decreases as more parameters are added into a model, but the ability to precisely estimate those model parameters also decreases. When too many parameters are included in a model the model is said to be overfitted or overparameterized, whereas when too few parameters are included, the model is said to be underfitted. Overfitting produces parameter estimates that have larger variances than the simpler model, both in the parameter estimates and in predicted values. Underfitting results in biased parameter estimates and biased prediction estimates. As model complexity increases,... [Pg.21]

Therefore, in machine learning work, we have two enemies underfitting and overfitting. The enlargement of the scope of hypothesis functions can only avoid the underfitting problem. However, it often makes overfitting becoming more serious problem. [Pg.12]

As a newly developed method for chemical data processing, SVM has following obvious advantages in comparison with classical chemometrical methods (1) It can treat both linear and nonlinear data sets so the trouble of underfitting can be depressed or controlled in some problems (2) It is so designed that the overfitting can be depressed or controlled by the capacity control of the indicator functions used so that the prediction results are often more reliable (3) As compared with ANN, SVM has no local minimum problem and the solution is unique. As... [Pg.21]


See other pages where Overfitting and underfitting is mentioned: [Pg.124]    [Pg.407]    [Pg.480]    [Pg.338]    [Pg.124]    [Pg.407]    [Pg.480]    [Pg.338]    [Pg.177]    [Pg.203]    [Pg.260]    [Pg.262]    [Pg.165]    [Pg.257]    [Pg.48]    [Pg.343]    [Pg.343]    [Pg.21]    [Pg.22]    [Pg.236]    [Pg.122]    [Pg.679]    [Pg.6]    [Pg.8]    [Pg.9]    [Pg.12]    [Pg.353]    [Pg.46]   


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