An unpruned model with very small node sizes may be overfit
to the specific dataset used to develop the model and thus may not be as useful for predicting classifications with different data.
The artworks created by an identical artist can overfit
our classifier to his/her drawing style.
We spatially rarefied the data to one point per 51.8 ha (0.25 [mi.sup.2]) using the Spatially Rarefy tool in the SDM toolbox (Brown 2014) in order to eliminate spatial clusters that could cause the model to be overfit
to the environmental biases of those points (Boria et al.
In order to create a multivariate model that would not be "overfit
" for logistic regression, a model-building strategy was utilized (Hosmer & Lemeshow, 2005).
The key to this result is the control of model complexity through regularization, a machine learning technique that yields a model complex enough to avoid underfitting the data but not so complex as to overfit
models are derived from and tested on idiosyncratic data and therefore do not replicate.
Estimating a logit model with 544 coefficients could lead to parameter estimates that overfit
the data in sample, while performing poorly out of sample.
It's possible to overfit
by making a single model that's too malleable, but the same result can be achieved by cooking up a different explanation for every different piece of data that you want to explain.
One possible reason is that large decision trees may overfit
SVMs are very often used as single models, because with boosting they tend to overfit
However, when the network begins to overfit
(a situation arising when ANN works well only with the training data) the data, the error on the test set will typically begin to increase.
This is because the complicated model is easy to overfit
. In our experiment, 200 to 400 neurons per layer is a good choice.