【深度学习】实验4 交叉验证

交叉验证

python 版本 3.7

scikit-learn 版本 1.0.2

代码

from sklearn.model_selection import train_test_split,cross_val_score,cross_validate # 交叉验证所需的函数
from sklearn.model_selection import KFold,LeaveOneOut,LeavePOut,ShuffleSplit # 交叉验证所需的子集划分方法
from sklearn.model_selection import StratifiedKFold,StratifiedShuffleSplit # 分层分割
from sklearn.model_selection import GroupKFold,LeaveOneGroupOut,LeavePGroupsOut,GroupShuffleSplit # 分组分割
from sklearn.model_selection import TimeSeriesSplit # 时间序列分割
from sklearn import datasets  # 自带数据集
from sklearn import svm  # SVM算法
from sklearn import preprocessing  # 预处理模块
from sklearn.metrics import recall_score  # 模型度量

iris = datasets.load_iris()  # 加载数据集
print('样本集大小：',iris.data.shape,iris.target.shape)

# ===================================数据集划分,训练模型==========================
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.4, random_state=0)  # 交叉验证划分训练集和测试集.test_size为测试集所占的比例
print('训练集大小：',X_train.shape,y_train.shape)  # 训练集样本大小
print('测试集大小：',X_test.shape,y_test.shape)  # 测试集样本大小
clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train) # 使用训练集训练模型
print('准确率：',clf.score(X_test, y_test))</