引导聚合算法(装袋;bagging)的python实现 # 输入csv格式的数据集,输出模型的平均准确率 import pandas as pd import numpy as np from sklearn.model_selection import KFold from sklearn.tree import DecisionTreeClassifier from sklearn.metric
import os os.chdir('../') from ml_models import utils import copy import numpy as np from ml_models.tree import CARTClassifier """ bagging分类实现,封装到ml_models.ensemble """ class BaggingClassifier(object): def __init__(self, base_estimator=None, n_estimators=10): """ :param base...
以下是一个基于Python实现Bagging算法的分点解答,包括必要的代码片段: 导入必要的Python库: 为了实现Bagging算法,我们需要导入一些必要的Python库,如numpy用于数组操作,sklearn中的DecisionTreeClassifier或DecisionTreeRegressor作为基学习器。 python import numpy as np from sklearn.tree import DecisionTreeClassifier, ...
实现Bagging算法的代码如下: fromsklearn.ensembleimportBaggingClassifierfromsklearn.treeimportDecisionTreeClassifierfromsklearn.preprocessingimportStandardScalerimportcsvfromsklearn.cross_validationimporttrain_test_splitfromsklearn.metricsimportaccuracy_scorefromsklearn.metricsimportconfusion_matrixfromsklearn.metricsimportcl...
VotingClassifier:用于分类的投票组合模型方法库 步骤2: 数据审查预处理函数 #基本状态查看 def set_summary(df): ''' 查看数据集后两条数据、数据类型、描述统计 :param df: 数据框 :return: 无 ''' print('{:-^60}'.format('Data overview:')) print(df.tail(2)) print('{:-^60}'.format('Data...
fromsklearn.treeimportDecisionTreeClassifierdeftrain_base_model(train_data):model=DecisionTreeClassifier()model.fit(train_data[:,:-1],train_data[:,-1])returnmodel 1. 2. 3. 4. 5. 6. 步骤3:模型集成 defbagging_predict(models,test_data):predictions=[]formodelinmodels:pred=model.predict(test...
Python 中的sklearn库有一种AdaBoostClassifier方法,用于将特征分类为有毒或可食用。 它的参数如下: base_estimator: boosted ensemble 是根据这个参数构建的。如果None,则值为DecisionTreeClassifier(max_depth=1)。 n_estimators:估计器的上限,默认值为 50 终止提升。如果完美拟合,则提前停止学习。
for i in range(10): rfc = RandomForestClassifier(n_estimators=25) rfc_s = cross_val_score(rfc,wine.data,wine.target,cv=10).mean() rfc_l.append(rfc_s) clf = DecisionTreeClassifier() clf_s = cross_val_score(clf,wine.data,wine.target,cv=10).mean() ...
To see how the Bagging Classifier performs with differing values of n_estimators we need a way to iterate over the range of values and store the results from each ensemble. To do this we will create a for loop, storing the models and scores in separate lists for later visualizations....
fromsklearn.treeimportDecisionTreeClassifier# 训练多个决策树模型models=[]for_inrange(10):# 训练10个模型sampled_X,sampled_y=bootstrap_sample(X_train,y_train)model=DecisionTreeClassifier()model.fit(sampled_X,sampled_y)models.append(model)# 预测y_pred=bagging_predict(models,X_test)print("预测结果...