用Python实现线性分类

线性分类是一种基本的机器学习算法，用于将数据点分为两个或多个类别。在Python中，可以使用scikit-learn库实现线性分类。

以下是实现线性分类的基本步骤：

1. 导入所需的库和数据集。

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

iris = datasets.load_iris()
X = iris.data[:, :2]  # 只使用前两个特征
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y)

2. 创建逻辑回归模型。

clf = LogisticRegression()

3. 训练模型。

clf.fit(X_train, y_train)

4. 使用模型进行预测。

y_pred = clf.predict(X_test)

5. 评估模型的性能。

from sklearn.metrics import accuracy_score

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

完整代码如下：

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

iris = datasets.load_iris()
X = iris.data[:, :2]  # 只使用前两个特征
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y)

clf = LogisticRegression()
clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)