Logistic回归——用于二分类。使用python在乳腺癌数据集上应用Logistic回归进行预测并分析。并且画出图像

首先，我们需要导入需要的库，包括numpy、pandas、sklearn和matplotlib。

import numpy as np
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt

接下来，我们将加载乳腺癌数据集并将其拆分为训练集和测试集。

data = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)

然后，我们可以创建并训练我们的Logistic回归模型。

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

现在我们可以使用测试集评估模型的性能。

accuracy = model.score(X_test, y_test)
print("Test Accuracy:", accuracy)

最后，我们可以使用Matplotlib绘制特征系数的条形图，以便了解哪些特征对结果的影响最大。

coef = model.coef_[0]
names = data.feature_names
plt.bar(range(len(coef)), coef)
plt.xticks(range(len(coef)), names, rotation=90)
plt.show()

完整代码如下：

import numpy as np
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt

data = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)

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

accuracy = model.score(X_test, y_test)
print("Test Accuracy:", accuracy)

coef = model.coef_[0]
names = data.feature_names
plt.bar(range(len(coef)), coef)
plt.xticks(range(len(coef)), names, rotation=90)
plt.show()