##载入相关模块
import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from collections import Counter
##载入数据
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['label'] = iris.target
df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']
##提取特征和样品
#取前面100个数，第一列、第二列和最后一列
data = np.array(df.iloc[:100, [0, 1, -1]])
#最后一个特征作为标签，其他的作为特征
X, y = data[:,:-1], data[:,-1]
#取80%作为训练，20%作为测试
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
载入sklearn中的支持向量分类器模块
from sklearn.ensemble import AdaBoostClassifier
##模型训练
clf = AdaBoostClassifier(n_estimators=100, learning_rate=0.5)
clf.fit(X_train, y_train)
验证算法精度
clf.score(X_test, y_test)
