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1、将数据集划分为训练集和测试集,假设有10000个样本,训练集占(80%,8000),测试集占(20%,2000)。然后再将训练集划分为训练集和验证集,其中训练集占70%,验证集占(30%,2400)。
2、通过K个同质或不同质的基训练器,以训练集数据进行拟合,将拟合得到的模型对验证集和测试集数据进行预测,将拟合结果作为新的变量标签加入各样本。 3、这时,每个样本都有K个基础模型预测结果的变量,将这K个变量作为自变量,利用验证集数据去拟合预测目标变量,得到第二层模型 4、使用第二层模型对验证集数据进行预测,根据结果报出准确率等一系列评价指标。优势:方法简单,易于理解
缺点:第二层模型的拟合只用了全部样本的24%,没有充分的利用样本信息。# 加载相关工具包 import numpy as np import pandas as pd import matplotlib.pyplot as plt plt.style.use("ggplot") %matplotlib inline import seaborn as sns
# 创建数据 from sklearn import datasets from sklearn.datasets import make_blobs from sklearn.model_selection import train_test_split data, target = make_blobs(n_samples=10000, centers=2, random_state=1, cluster_std=1.0 ) ## 创建训练集和测试集 X_train1,X_test,y_train1,y_test = train_test_split(data, target, test_size=0.2, random_state=1) ## 创建训练集和验证集 X_train,X_val,y_train,y_val = train_test_split(X_train1, y_train1, test_size=0.3, random_state=1) print("The shape of training X:",X_train.shape) print("The shape of training y:",y_train.shape) print("The shape of test X:",X_test.shape) print("The shape of test y:",y_test.shape) print("The shape of validation X:",X_val.shape) print("The shape of validation y:",y_val.shape)
# 设置第一层分类器 from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier from sklearn.neighbors import KNeighborsClassifier clfs = [SVC(probability = True),RandomForestClassifier(n_estimators=5, n_jobs=-1, criterion='gini'),KNeighborsClassifier()] # 设置第二层分类器 from sklearn.linear_model import LinearRegression lr = LinearRegression()
# 输出第一层的验证集结果与测试集结果 val_features = np.zeros((X_val.shape[0],len(clfs))) # 初始化验证集结果 test_features = np.zeros((X_test.shape[0],len(clfs))) # 初始化测试集结果 for i,clf in enumerate(clfs): clf.fit(X_train,y_train) val_feature = clf.predict_proba(X_val)[:, 1] test_feature = clf.predict_proba(X_test)[:,1] val_features[:,i] = val_feature test_features[:,i] = test_feature
# 将第一层的验证集的结果输入第二层训练第二层分类器 lr.fit(val_features,y_val) # 输出预测的结果 from sklearn.model_selection import cross_val_score cross_val_score(lr,test_features,y_test,cv=5)
使用Blending方式对iris数据集进行预测,并用决策边界画出来。
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