Mobile user experience classification based on random forest model

In the future, various factors affecting user satisfaction will be analyzed through the survey results to provide the basis for decision-making, to achieve earlier and more comprehensive improvement in user satisfaction. This paper constructs a random forest model to explore the influencing factors of mobile user scoring. Foremost, preprocess the acquired data. Different data are processed differently. The continuous variables are inserted by mean value. Delete obvious abnormal samples. The string variable uses mode to fill in or delete the sample, which is explained in detail in the text. Label coding and unique coding are carried out for the category feature data, feature construction is carried out, and the information field of opinion type (non-stereotyped) is deleted. A random forest classification model is established for the processed data to predict the sample score. In order to make the model more optimized, the Bayesian parameter adjustment method is used to make the model achieve the optimal effect. The results show that the random forest algorithm plays an important role in user rating prediction and will play an important role in service improvement and guiding the development direction in the future.