使用cross_val_predict sklearn计算评估指标

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_predict, StratifiedKFold
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# 加载数据集
iris = load_iris()
X, y = iris.data, iris.target

# 定义模型
model = SVC()

# 使用 Stratified K-Fold 交叉验证
cv = StratifiedKFold(n_splits=5)

# 生成预测值
y_pred = cross_val_predict(model, X, y, cv=cv)

# 计算评估指标
accuracy = accuracy_score(y, y_pred)
precision = precision_score(y, y_pred, average='weighted')
recall = recall_score(y, y_pred, average='weighted')
f1 = f1_score(y, y_pred, average='weighted')

print(f'Accuracy: {accuracy}')
print(f'Precision: {precision}')
print(f'Recall: {recall}')
print(f'F1 Score: {f1}')

from imblearn.over_sampling import SMOTE

# 使用 SMOTE 进行过采样
smote = SMOTE(random_state=42)
X_resampled, y_resampled = smote.fit_resample(X, y)

# 使用重采样后的数据进行交叉验证
y_pred_resampled = cross_val_predict(model, X_resampled, y_resampled, cv=cv)