导入breast cancer 数据集python代码
时间: 2023-04-11 08:00:42 浏览: 141
可以使用以下代码导入breast cancer数据集:
```python
from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
X = data.data
y = data.target
```
这将导入breast cancer数据集,并将数据存储在X和y变量中。
相关问题
breast cancer 数据集分析代码
以下是使用Python和Scikit-learn库进行乳腺癌数据集分析的代码:
```python
# 导入所需的库和函数
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
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
# 加载乳腺癌数据集
cancer = load_breast_cancer()
# 将数据集转换为DataFrame
df = pd.DataFrame(np.c_[cancer['data'], cancer['target']], columns = np.append(cancer['feature_names'], ['target']))
# 拆分数据集为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(df[cancer['feature_names']], df['target'], test_size=0.20, random_state=42)
# 定义逻辑回归模型并训练
logreg = LogisticRegression()
logreg.fit(X_train, y_train)
# 对测试集进行预测
y_pred = logreg.predict(X_test)
# 计算模型准确率和混淆矩阵
print(f"Accuracy: {accuracy_score(y_test, y_pred)}")
print(f"Confusion Matrix:\n{confusion_matrix(y_test, y_pred)}")
```
这段代码将乳腺癌数据集加载到DataFrame中,并将其拆分为训练集和测试集。然后,它定义了一个逻辑回归模型,并使用训练数据拟合该模型。最后,它使用测试集进行预测,并计算了模型的准确率和混淆矩阵。
给出SVM 回归 with Breast Cancer Dataset的Python代码
以下是使用SVM回归算法对Breast Cancer数据集进行预测的Python代码:
```python
# 导入必要的库
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error
# 加载Breast Cancer数据集
breast_cancer = datasets.load_breast_cancer()
# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(breast_cancer.data, breast_cancer.target, test_size=.2, random_state=42)
# 创建SVM回归模型
svm_reg = SVR(kernel='linear', C=1, epsilon=.1)
# 训练模型
svm_reg.fit(X_train, y_train)
# 预测测试集
y_pred = svm_reg.predict(X_test)
# 计算均方误差
mse = mean_squared_error(y_test, y_pred)
print("均方误差:", mse)
```
以上代码使用线性核函数创建SVM回归模型,并使用Breast Cancer数据集进行训练和测试。最后,计算预测结果与真实结果之间的均方误差。