有没有办法只包括在逻辑回归中向后消除时P <0.05显着的因子

from sklearn.linear_model import LogisticRegression

def backward_selection(features, target, correlation_matrix):

from sklearn.linear_model import LogisticRegression

# 训练逻辑回归模型
model = LogisticRegression()
model.fit(features, target)

# 使用向后消除方法选择特征
selected_features = []
for i in range(len(features)):
    feature = features[i]
    # 计算特征和目标变量之间的相关系数
    correlation = correlation_matrix[feature][target]
    # 如果相关系数大于阈值，则将该特征添加到选定特征列表中
    if correlation > threshold:
        selected_features.append(feature)

# 返回选定特征列表
return selected_features

# 示例数据
features = [[1, 2], [3, 4], [5, 6], [7, 8]]
target = [1, 0, 1, 0]

# 定义相关系数矩阵
correlation_matrix = [[1, 0.8, 0.2, 0],
                     [0.8, 1, 0.5, 0],
                     [0.2, 0.5, 1, 0],
                     [0, 0, 0, 1]]

# 调用向后消除函数
selected_features = backward_selection(features, target, correlation_matrix)

# 输出选定特征列表
print(selected_features)