دانلود گزارش کارورزی و کار آموزی

چکیده : 

Kernel ridge regression is a technique to perform ridge regression with a potentially infinite

number of nonlinear transformations of the independent variables as regressors. This

method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable

in many different contexts. The influence of the choice of kernel and the setting of tuning

parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including

polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels

are interpreted in terms of their smoothing properties, and the tuning parameters associated

to all these kernels are related to smoothness measures of the prediction function

and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for

selecting the tuning parameters from small grids using cross-validation. A Monte Carlo

study confirms the practical usefulness of these rules of thumb. Finally, the flexible and

smooth functional forms provided by the Gaussian and Sinc kernels make them widely

applicable. Therefore, their use is recommended instead of the popular polynomial kernels

in general settings, where no information on the data-generating process is available.