Wine数据集实现adaboost算法的python代码

以下是使用Wine数据集实现adaboost算法的Python代码。首先我们需要导入所需的库和数据集：

import numpy as np
from sklearn.ensemble import AdaBoostClassifier
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

wine = load_wine()
X = wine.data
y = wine.target

接下来，我们将数据集分为训练集和测试集：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

然后，我们可以使用`AdaBoostClassifier`类来创建adaboost分类器：

ada = AdaBoostClassifier(n_estimators=50, learning_rate=1)

在这里，我们选择了50个基本分类器，并将学习率设置为1。现在，我们可以使用训练数据来训练adaboost分类器：

ada.fit(X_train, y_train)

最后，我们可以使用测试数据来评估adaboost分类器的性能：

y_pred = ada.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

完整的代码如下：

import numpy as np
from sklearn.ensemble import AdaBoostClassifier
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

wine = load_wine()
X = wine.data
y = wine.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

ada = AdaBoostClassifier(n_estimators=50, learning_rate=1)
ada.fit(X_train, y_train)

y_pred = ada.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)