I am using XLSTAT tool to get the ML results. For the classification and regression trees, I am getting the results given in the image [https://i.stack.imgur.com/fa8mw.png]. I am not sure about the interpretation of the test statistic and p-value given in the table. Can I rely on them to say whether the prediction model is significant. For example, because p-value is less than 0.05, the model is statistically significant predictor? If yes, my problem would be, what about the other nodes in the model. Should I take my conclusion based on the p-value for the main node (node 1) only?
Regarding classification and regression random forest, XLSTS provides me with variable importance (mean decrease accuracy). Based on this number, I am not sure how to decide whether the dependent variable is a good predictor. For example, what if that number is negative, what if it is between 0 and 1, ..etc. Is there a rule of thumb?