1. class sklearn.ensemble.RandomForestClassifier(n_estimators=10, crite-rion=’gini’, max_depth=None, 2. min_samples_split=2, min_samples_leaf=1, 3. min_weight_fraction_leaf=0.0, 4. max_features=’auto’, 5. max_leaf_nodes=None, bootstrap=True, 6. oob_score=False, n_jobs=1, ...
sklearn RandomForestClassifier class_weight参数说明和metrics average参数说明,程序员大本营,技术文章内容聚合第一站。
Chen, Chao, Andy Liaw, and Leo Breiman. “Using random forest to learn imbalanced data.” Unive...
classsklearn.ensemble.RandomForestClassifier(n_estimators=100,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=None...
sklearn中的随机森林是基于RandomForestClassifier类实现的,它的原型是 class RandomForestClassifier(ForestClassifier) 继承了一个抽象类ForestClassifier,也就是分类树 RandomForestClassifier有若干个参数,下面我们一个个来看: n_estimators 随机森林中树的个数 默认为10 ...
verbose=0, warm_start=False, class_weight=None) 随机森林分类器 from sklearn.tree import RandomForestClassifier #导入需要的模块 rfc = RandomForestClassifier() #实例化 rfc = rfc.fit(X_train,y_train) #用训练集数据训练模型 result = rfc.score(X_test,y_test) #导入测试集,从接口中调用需要的信...
n_informative=2, n_redundant=0, random_state=0, shuffle=False) 1. 2. 3. 4. 5. clf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0) clf.fit(X, y) 1. 2. RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', ...
classsklearn.ensemble.RandomForestClassifier(n_estimators=100, *, 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, ...
(RandomForestClassifier) 参数: 1、n_estimators : integer, optional (default=10),森林里树的个数。 2、criterion : string, optional (default=“gini”),衡量分割质量的函数。支持的标准是基尼系数“gini”,以及信息增益的熵“ entropy”。注意,这个参数是树特有的。
本文简要介绍python语言中sklearn.ensemble.RandomForestClassifier的用法。 用法: classsklearn.ensemble.RandomForestClassifier(n_estimators=100, *, 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, mi...