measures how homogenous a node is in tree-based models. Read more about it here
Interpreting p-values can be tricky. There are two approaches: the Fisher’s way and Neyman-Pearson’s way.
Neyman-Pearson [NP] said that you pick a cutoff and you use it. It’s less than the cutoff(say, 0.05) or it’s not. There’s no other information to convey. In NP, 0.08 and 0.97 are the same.
Fisher said you take the p-value and you treat it as the level of evidence that there is an effect. <0.2 is some evidence, but it’s pretty weak; <0.1 is a bit better, but still kind of weak. <0.05 is what Fisher said is often good enough (but he also wrote that one should change one’s significance level according to the situation, which no one does).