机器学习与数据挖掘-实验七

编程实现AdaBoost算法

编码实现AdaBoost算法，以决策树为基学习器，训练一个AdaBoost集成模型，对测试样本进行判别。

from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import numpy as np

data = np.loadtxt('wine.data',delimiter=',')

X = data[:,1:]
y = data[:,0:1]
X_train,X_test,y_train,y_test = train_test_split(X,y.ravel(),train_size=0.8,random_state=0)

rf=DecisionTreeClassifier()

model=AdaBoostClassifier(base_estimator=rf,n_estimators=50,learning_rate=0.5)
model.fit(X_train,y_train)

y_train_hat = model.predict(X_train)
print("train accuarcy:",accuracy_score(y_train,y_train_hat))

y_test_hat=model.predict(X_test)
print("test accuarcy:",accuracy_score(y_test,y_test_hat))