1. 导入需要的库 from sklearn.datasets import load_breast_cancer from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import GridSearchCV from sklearn.model_selection import cross_val_score import matplotlib.pyplot as plt import pandas as pd 1. 2. 3. 4. 5. 6. 2....
1.导入需要的库 from sklearn.datasets import load_breast_cancer #随机森林分类器 from sklearn.ensemble import RandomForestClassifier #网格搜索 from sklearn.model_selection import GridSearchCV #交叉验证 from sklearn.model_selection import cross_val_score #画图用的 import matplotlib.pyplot as plt impor...
fromsklearn.datasetsimportload_breast_cancer cancer = load_breast_cancer() X, y = cancer.data, cancer.target print(f"特征数量:{X.shape[1]}") print(f"样本数量:{len(y)},其中0代表良性,1代表恶性")
例子: 假设您对样本 10、50 和 85 感兴趣,并想知道它们的类名。 >>> from sklearn.datasets import load_breast_cancer >>> data = load_breast_cancer() >>> data.target[[10, 50, 85]] array([0, 1, 0]) >>> list(data.target_names) ['malignant', 'benign']相关...
from sklearn import datasets # 导入库 cancer = datasets.load_breast_cancer() # 导入乳腺癌数据 真实世界中的数据集 scikit-learn 提供加载较大数据集的工具,并在必要时可以在线下载这些数据集,用datasets.fetch_xx()加载。 调用描述fetch_olivetti_faces()Olivetti 脸部图片数据集fetch_20newsgroups()用于...
#1.1.3 糖尿病数据集[回归预测]fromsklearn.datasetsimportload_diabetesimportpandas as pdimportmatplotlib.pyplot as plt diabetes_data_bunch=load_diabetes() #print("数据集说明:",diabetes_data_bunch.DESCR) # 比较详细的数据,很长print("特征名:",diabetes_data_bunch.feature_names) ...
可以使用sklearn.datasets模块的load_iris函数直接从sklearn加载鸢尾花数据集。 代码语言:javascript 代码运行次数:0 复制 Cloud Studio代码运行 # To install sklearn pip install scikit-learn # Toimportsklearn from sklearn.datasetsimportload_iris # Load the iris dataset ...
from sklearn.datasets import load_breast_cancerfrom sklearn.feature_selection import SelectFdr, chi2X, y = load_breast_cancer(return_X_y=True)X_new = SelectFdr(chi2, alpha=0.01).fit_transform(X, y)X.shape, X_new.shape# ((569, 30), (569, 16))复制代码 ...
数据集对象cancer有一个属性.data,它包含了特征数据。我们可以将其赋值给变量data。 (可选)从cancer中提取标签数据,并将其存储: 数据集对象cancer有一个属性.target,它包含了标签数据。根据用户需要,可以将其赋值给另一个变量(例如target)。 下面是具体的代码实现: python from sklearn.datasets import load_breast...
from sklearn.datasets import load_breast_cancer data_set = load_breast_cancer() x = torch.from_numpy(data_set['data']) y = torch.from_numpy(data_set['target']) x = x.float() y = y.float() y = y.unsqueeze(dim=1) print(x.shape) ...