classsklearn.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, ...
开发环境 jupyter notebook from sklearn.datasets import load_iris from sklearn import model_selection from sklearn.ensemble import RandomForestClassifier from sklearn.grid_search import 1. 2. 3. 4. iris=load_iris() x=iris.data y=iris.target X_train,X_test,y_train,y_test =...
classsklearn.ensemble.RandomForestClassifier(n_estimators=10, crite-rion=’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, bootstrap=True, oob_score=False, n_jobs=1, ran-dom_state=None, verbose=...
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=...
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_sc...
from sklearn.tree import DecisionTreeClassifier 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 ...
# Random Forest Classifier Example from matplotlib import pyplot as plt from sklearn.datasets import load_iris from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import plot_confusion_matrix from sklearn.model_selection import train_test_split def main(): # Load Iris dataset ...
随机森林是一种集成学习方法(ensemble),由许多棵决策树构成的森林共同来进行预测。为什么叫“随机”森林呢?随机主要体现在以下两个方面: 1.每棵树的训练集是随机且有放回抽样产生的; 2.训练样本的特征是随机选取的。 fromsklearn.ensembleimportRandomForestClassifierfromsklearn.datasetsimportmake_classification ...
fromsklearn.datasetsimportload_iris iris=load_iris()X,y=iris.data,iris.target 这里的 X 表示样本特征,y 表示样本标签。 模型训练 我们可以使用 scikit-learn 库中的 RandomForestClassifier 类来构建随机森林分类器。在训练时可以指定一些参数,例如树的数量,每棵树的最大深度等等。
ConfusionMatrixDisplay#导入模型fromsklearn.ensembleimportRandomForestClassifierfromsklearn.ensembleimportBaggingClassifierfromsklearn.treeimportDecisionTreeClassifier# 生成数据fromsklearn.datasetsimportmake_classificationX,y=make_classification(n_samples=5000,n_features=30,n_informative=25,n_redundant=5,n_classes=...