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。

好的，下面是关于在乳腺癌数据集上应用Logistic回归进行预测和分析的Python代码：

首先，我们需要导入所需的库和数据集：

import pandas as pd
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix

cancer = load_breast_cancer()
df = pd.DataFrame(np.c_[cancer['data'], cancer['target']], columns = np.append(cancer['feature_names'], ['target']))

然后，我们需要将数据集分成训练集和测试集：

X = df.iloc[:, :-1].values
y = df.iloc[:, -1].values

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

接下来，我们可以使用Logistic回归模型进行训练和预测：

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

y_pred = model.predict(X_test)

最后，我们可以使用准确率和混淆矩阵来评估模型的性能：

print("Accuracy:", accuracy_score(y_test, y_pred))
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))

完整的代码如下所示：

import pandas as pd
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix

cancer = load_breast_cancer()
df = pd.DataFrame(np.c_[cancer['data'], cancer['target']], columns = np.append(cancer['feature_names'], ['target']))

X = df.iloc[:, :-1].values
y = df.iloc[:, -1].values

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

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

y_pred = model.predict(X_test)

print("Accuracy:", accuracy_score(y_test, y_pred))
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))