RF参数

class sklearn.ensemble.RandomForestClassifier(n_estimators=10, 
criterion=’gini’, max_depth=None, min_samples_split=2, 
min_samples_leaf=1, min_weight_fraction_leaf=0.0, 
max_features=’auto’, max_leaf_nodes=None, 
min_impurity_decrease=0.0, min_impurity_split=None, 
bootstrap=True, oob_score=False, n_jobs=1, random_state=None, 
verbose=0, warm_start=False, class_weight=None)

sklearn官方文档–RF

# n_estimators，森林中树的个数
# criterion ，”gini”，“entropy” 
# max_features ，划分时考虑的特征的数量，默认是sqrt(n_feature)
# max_depth
# min_samples_split
# min_samples_leaf 
# min_weight_fraction_leaf
# max_leaf_nodes
# min_impurity_split 
# min_impurity_decrease
# bootstrap 是否有放回的抽样
# n_jobs # 并行的个数
# class_weight #类不均衡的时候使用

feature_importances_: 特征的重要性

RF文档