oob_score : bool (default=False) Whether to use out-of-bag samples to estimate the generalization accuracy. oob_score: bool(默认=False)是否使用袋外样品进行估算泛化精度。
特征越重要，因此，可以根据特征重要性排序，然后选择最佳特征组合；RandomForestClassifier(n_estimators=200,oob_score=True)oob_score : bool (default=False) Whether to use out-of-bag samples to estimate the generalization accuracy.oob_score: bool(默认=False) 是否使⽤袋外样品进⾏估算泛化精度。
a different take on the question: to start with, you have to associate a loss with every misclassification you do. This price-paid/loss/penalty for misclassification would(probably) be different for False Positive(FP) vs False Negatives(FN). Some classifications, say cancer ...