机器学习—集成学习（Adaboost）

一、原理部分：

二、sklearn实现：

from sklearn.ensemble import AdaBoostClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
import numpy as np
from sklearn.tree import DecisionTreeClassifier

digits = load_digits()
x_data = digits.data
y_data = digits.target
x_train,x_test,y_train,y_test = train_test_split(x_data,y_data,random_state = 1)

#第一轮，确定n
adaboost = AdaBoostClassifier(DecisionTreeClassifier(max_depth=6),n_estimators=150)
model_ada = GridSearchCV(adaboost,param_grid=({'learning_rate':[0.7,0.8]}))
model_ada.fit(x_train,y_train)
print(model_ada.best_params_)
y_hat = model_ada.predict(x_test)
print(accuracy_score(y_hat,y_test))

#adaboost中没有max_depth参数，还要自己加入DecisionTreeClassifier