提升方法就是组合一系列弱分类器构成一个强分类器,AdaBoost是其代表性算法
AdaBoost算法
适用问题:二类分类,要处理多类分类需进行改进
代码(用sklearn实现):
# encoding=utf-8
import pandas as pd
import time
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.ensemble import AdaBoostClassifier
if __name__ == '__main__':
print("Start read data...")
time_1 = time.time()
raw_data = pd.read_csv('../data/train_binary.csv', header=0)
data = raw_data.values
features = data[::, 1::]
labels = data[::, 0]
# 随机选取33%数据作为测试集,剩余为训练集
train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.33, random_state=0)
time_2 = time.time()
print('read data cost %f seconds' % (time_2 - time_1))
print('Start training...')
# n_estimators表示要组合的弱分类器个数;
# algorithm可选{‘SAMME’, ‘SAMME.R’},默认为‘SAMME.R’,表示使用的是real boosting算法,‘SAMME’表示使用的是discrete boosting算法
clf = AdaBoostClassifier(n_estimators=100,algorithm='SAMME.R')
clf.fit(train_features,train_labels)
time_3 = time.time()
print('training cost %f seconds' % (time_3 - time_2))
print('Start predicting...')
test_predict = clf.predict(test_features)
time_4 = time.time()
print('predicting cost %f seconds' % (time_4 - time_3))
score = accuracy_score(test_labels, test_predict)
print("The accruacy score is %f" % score)
代码可从这里AdaBoost/AdaBoost_sklearn.py获取
实验数据为train.csv的运行结果:
实验数据为train_binary.csv的运行结果: