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Greedy Search Methods
Published in Max Kuhn, Kjell Johnson, Feature Engineering and Selection, 2019
When the predictor is numeric, the following options exist: When the outcome is categorical, the same tests can be used in the case above where the predictor is categorical and the outcome is numeric. The roles are simply reversed in the t-test, curve calculations, F-test, and so on. When there are a large number of tests or if the predictors have substantial multicollinearity, the correlation-adjusted t-scores of Opgen-Rhein and Strimmer (2007) and Zuber and Strimmer (2009) are a good alternative to simple ANOVA statistics.When the outcome is numeric, a simple pairwise correlation (or rank correlation) statistic can be calculated. If the relationship is nonlinear, then the maximal information coefficient (MIC) values (Reshef et al., 2011) or A statistics (Murrell et al., 2016), which measure strength of the association between numeric outcomes and predictors, can be used.Alternatively, a generalized additive model (GAM) (Wood, 2006) can fit nonlinear smooth terms to a set of predictors simultaneously and measure their importance using a p-value that tests against a null hypothesis that there is no trend of each predictor. An example of such a model with a categorical outcome was shown in Figure 4.15(b).
Short-term office building elevator energy consumption forecast using SARIMA
Published in Journal of Building Performance Simulation, 2020
Ane Blázquez-García, Angel Conde, Aitor Milo, Roberto Sánchez, Irantzu Barrio
A Generalized Additive Model (GAM) is a generalized linear model where the response variable is modelled by independent variables, which are in the form of some smooth functions. It can be formally defined as follows: where is the response variable (an exponential family distribution is specified for it), N is the number of data-points, is the expected value of for , g is a link function (e.g. identity or log functions), are independent variables, is an intercept and are unknown smooth functions.