首页逻辑回归做乳腺癌分析python代码

逻辑回归做乳腺癌分析python代码

时间: 2023-09-12 12:02:54 浏览: 289

下面是使用Python进行逻辑回归分析乳腺癌数据集的代码：首先需要导入所需的库： ```python import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score ``` 然后读取数据集： ```python data = pd.read_csv("breast_cancer_data.csv") ``` 接着，将数据集分为训练集和测试集： ```python X = data.drop('diagnosis', axis=1) y = data['diagnosis'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) ``` 使用逻辑回归模型拟合训练集： ```python logreg = LogisticRegression() logreg.fit(X_train, y_train) ``` 使用测试集评估模型准确率： ```python y_pred = logreg.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print("Accuracy of Logistic Regression model:", accuracy) ``` 完整代码如下： ```python import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score # 读取数据集 data = pd.read_csv("breast_cancer_data.csv") # 分为训练集和测试集 X = data.drop('diagnosis', axis=1) y = data['diagnosis'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 拟合逻辑回归模型 logreg = LogisticRegression() logreg.fit(X_train, y_train) # 评估模型准确率 y_pred = logreg.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print("Accuracy of Logistic Regression model:", accuracy) ``` 注意：这里的数据集文件名为"breast_cancer_data.csv"，需要根据实际情况修改文件名。

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