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, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=...
2.训练样本的特征是随机选取的。 fromsklearn.ensembleimportRandomForestClassifierfromsklearn.datasetsimportmake_classification X,y=make_classification(n_samples=1000,n_features=4,n_informative=2,n_redundant=0,random_state=0,shuffle=False)clf=RandomForestClassifier(n_estimators=100,max_depth=2,random_sta...
from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from sklearn import metrics from sklearn.model_selection import KFold from sklearn.cross_validation import KFold def classification_model (model, data, ...
如果是classification,采用“投票”法选取得票最多的类作为集成学习器的结果; 如果是regression,采用“平均”方法获得均值作为集成学习器的结果。 分类:在scikit-learn中,RandomForest的分类是sklearn.ensemble.RandomForestClassifier,回归是sklearn.ensemble.RrandomForestRegressor。 对Bagging Methods算法在如下方面进行了改...
随机森林分类器的实现可以使用Python中的scikit-learn库。下面是一个简单的代码示例: 1 2 3 4 5 6 7 8 9 10 11 fromsklearn.ensembleimportRandomForestClassifier fromsklearn.datasetsimportmake_classification X, y=make_classification(n_samples=1000, n_features=4, ...
六、随机森林在Sklearn中的建模示例 6.1 参数总览 6.2 使用随机森林回归建模示例 七、随机森林在Sk...
以sklearn中的鸢尾花数据集为例进行说明。 1、加载必要的库 from sklearn.datasets import load_iris from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import GridSearchCV from sklearn.model_selection import train_test_split ...
利用Python的两个模块,分别为pandas和scikit-learn来实现随机森林。 from sklearn.datasets import load_iris from sklearn.ensemble import RandomForestClassifier import pandas as pd import numpy as np iris = load_iris() df = pd.DataFrame(iris.data, columns=iris.feature_names) ...
导入: from sklearn.ensemble import RandomForestClassifier 定义列车数据和目标数据: train = [[1,2,3],[2,5,1],[2,1,7]] target = [0,1,0] target中的值表示你要预测的标签。 启动RandomForest 对象并执行learn(fit): rf = RandomForestClassifier(n_estimators=100) rf.fit(train, target) ...
三、sklearn参数详解 classsklearn.ensemble.RandomForestClassifier(n_estimators=’warn’,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...