An improved ridge regression algorithm and its application in predicting TV ratings

Abstract

Ridge regression is a biased estimated regressive method, which is traditionally used in collinearity data analysis. It is actually a modified Least Square method, which gains more rational and reliable regression coefficient by giving up the unbiasedness of Least Squares Estimation, reducing partial information and decreasing accuracy to overcome the over-fitting problems. This article presents an improved ridge regression algorithm and utilizes it to predict the audience rating for TV ratings. It is tested by 10 - fold Cross Validation. TV rating is an important indication to measure the quality and user experience, as well as one of the vital standards to state the value of a TV channel. The improved ridge regression algorithm is used to learn the model of weight matrix, which is trained by the error algorithm to predict the TV ratings. The extensive experimental results demonstrate the effectiveness of the proposed algorithm.