# 机器学习算法总结-第六天(Adaboost算法)

SKlearn中的Adaboost使用

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主要调的参数：第一部分是对我们的Adaboost的框架进行调参，第二部分是对我们选择的弱分类器进行调参。
使用 Adaboost 进行手写数字识别
导入库，载入数据

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier

from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import train_test_split
from sklearn.model_selection import learning_curve

from sklearn.datasets import load_digits
dataset = load_digits()
X = dataset['data']
y = dataset['target']

看下图像：
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使用深度为 1 的决策树分类器,准确率是0.2641850696745583

reg_ada = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1))
scores_ada = cross_val_score(reg_ada, X, y, cv=6)
scores_ada.mean()

通过调节决策树的深度，提高识别准确率

score = []
for depth in [1,2,10] : 
    reg_ada = AdaBoostClassifier(DecisionTreeClassifier(max_depth=depth))
    scores_ada = cross_val_score(reg_ada, X, y, cv=6)
    score.append(scores_ada.mean())

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当决策树的深度为 10 时，分类器得到了最高的分类准确率 95%
详细参数参考下面这篇链接：
https://www.cnblogs.com/pinard/p/6136914.html