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Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. [1] In the context of machine learning and more generally statistical analysis , this may be the selection of a statistical model from a set of candidate models, given data.
The technique essentially involves using data from, for example, censuses relating to various types of people corresponding to different characteristics (e.g., age, race), in a first step to estimate the relationship between those types and individual preferences (i.e., multi-level regression of the dataset).
It is computationally just as fast as forward selection. It produces a full piecewise linear solution path, which is useful in cross-validation or similar attempts to tune the model. If two variables are almost equally correlated with the response, then their coefficients should increase at approximately the same rate.
Claeskens, G.; Hjort, N. L. (2008), Model Selection and Model Averaging, Cambridge University Press. [Note: the AIC defined by Claeskens & Hjort is the negative of the standard definition—as originally given by Akaike and followed by other authors.]
Similarly, for a regression analysis, an analyst would report the coefficient of determination (R 2) and the model equation instead of the model's p-value. However, proponents of estimation statistics warn against reporting only a few numbers. Rather, it is advised to analyze and present data using data visualization.
In statistics, Mallows's, [1] [2] named for Colin Lingwood Mallows, is used to assess the fit of a regression model that has been estimated using ordinary least squares.It is applied in the context of model selection, where a number of predictor variables are available for predicting some outcome, and the goal is to find the best model involving a subset of these predictors.
Overmatching, or post-treatment bias, is matching for an apparent mediator that actually is a result of the exposure. [12] If the mediator itself is stratified, an obscured relation of the exposure to the disease would highly be likely to be induced. [13] Overmatching thus causes statistical bias. [13]
In contrast to the case of best linear unbiased estimation, the "quantity to be estimated", ~, not only has a contribution from a random element but one of the observed quantities, specifically which contributes to ^, also has a contribution from this same random element.